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Nguyen Viet Tuan
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"""
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A2C, IA2C, MA2C models
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@author: Tianshu Chu
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"""
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import os
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from agents.utils import *
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from agents.policies import *
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import logging
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import multiprocessing as mp
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import numpy as np
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import tensorflow.compat.v1 as tf
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class A2C:
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def __init__(self, n_s, n_a, total_step, model_config, seed=0, n_f=None):
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# load parameters
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self.name = 'a2c'
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self.n_agent = 1
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# init reward norm/clip
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self.reward_clip = model_config.getfloat('reward_clip')
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self.reward_norm = model_config.getfloat('reward_norm')
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self.n_s = n_s
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self.n_a = n_a
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self.n_step = model_config.getint('batch_size')
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# init tf
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tf.reset_default_graph()
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tf.set_random_seed(seed)
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config = tf.ConfigProto(allow_soft_placement=True)
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self.sess = tf.Session(config=config)
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self.policy = self._init_policy(n_s, n_a, n_f, model_config)
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self.saver = tf.train.Saver(max_to_keep=5)
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if total_step:
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# training
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self.total_step = total_step
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self._init_scheduler(model_config)
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self._init_train(model_config)
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self.sess.run(tf.global_variables_initializer())
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def _init_policy(self, n_s, n_a, n_w, n_f, model_config, agent_name=None):
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n_fw = model_config.getint('num_fw')
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n_ft = model_config.getint('num_ft')
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n_lstm = model_config.getint('num_lstm')
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n_fp = model_config.getint('num_fp')
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policy = FPLstmACPolicy(n_s, n_a, n_w, n_f, self.n_step, n_fc_wave=n_fw,
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n_fc_wait=n_ft, n_fc_fp=n_fp, n_lstm=n_lstm, name=agent_name)
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return policy
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def _init_scheduler(self, model_config):
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lr_init = model_config.getfloat('lr_init')
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lr_decay = model_config.get('lr_decay')
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beta_init = model_config.getfloat('entropy_coef_init')
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beta_decay = model_config.get('entropy_decay')
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if lr_decay == 'constant':
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self.lr_scheduler = Scheduler(lr_init, decay=lr_decay)
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else:
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lr_min = model_config.getfloat('LR_MIN')
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self.lr_scheduler = Scheduler(
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lr_init, lr_min, self.total_step, decay=lr_decay)
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if beta_decay == 'constant':
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self.beta_scheduler = Scheduler(beta_init, decay=beta_decay)
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else:
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beta_min = model_config.getfloat('ENTROPY_COEF_MIN')
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beta_ratio = model_config.getfloat('ENTROPY_RATIO')
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self.beta_scheduler = Scheduler(beta_init, beta_min, self.total_step * beta_ratio,
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decay=beta_decay)
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def _init_train(self, model_config):
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# init loss
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v_coef = model_config.getfloat('value_coef')
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max_grad_norm = model_config.getfloat('max_grad_norm')
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alpha = model_config.getfloat('rmsp_alpha')
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epsilon = model_config.getfloat('rmsp_epsilon')
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self.policy.prepare_loss(v_coef, max_grad_norm, alpha, epsilon)
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# init replay buffer
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gamma = model_config.getfloat('gamma')
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self.trans_buffer = OnPolicyBuffer(gamma)
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def save(self, model_dir, global_step):
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self.saver.save(self.sess, model_dir + 'checkpoint',
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global_step=global_step)
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def load(self, model_dir, checkpoint=None):
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save_file = None
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save_step = 0
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if os.path.exists(model_dir):
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if checkpoint is None:
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for file in os.listdir(model_dir):
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if file.startswith('checkpoint'):
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prefix = file.split('.')[0]
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tokens = prefix.split('-')
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if len(tokens) != 2:
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continue
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cur_step = int(tokens[1])
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if cur_step > save_step:
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save_file = prefix
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save_step = cur_step
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else:
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save_file = 'checkpoint-' + str(int(checkpoint))
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if save_file is not None:
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self.saver.restore(self.sess, model_dir + save_file)
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logging.info('Checkpoint loaded: %s' % save_file)
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return True
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logging.error('Can not find old checkpoint for %s' % model_dir)
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return False
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def reset(self):
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self.policy._reset()
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def backward(self, R, summary_writer=None, global_step=None):
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cur_lr = self.lr_scheduler.get(self.n_step)
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cur_beta = self.beta_scheduler.get(self.n_step)
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obs, acts, dones, Rs, Advs = self.trans_buffer.sample_transition(R)
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self.policy.backward(self.sess, obs, acts, dones, Rs, Advs, cur_lr, cur_beta,
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summary_writer=summary_writer, global_step=global_step)
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def forward(self, ob, done, out_type='pv'):
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return self.policy.forward(self.sess, ob, done, out_type)
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def add_transition(self, ob, action, reward, value, done):
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# Hard code the reward norm for negative reward only
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if (self.reward_norm):
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reward /= self.reward_norm
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if self.reward_clip:
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reward = np.clip(reward, -self.reward_clip, self.reward_clip)
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self.trans_buffer.add_transition(ob, action, reward, value, done)
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class IA2C(A2C):
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def __init__(self, n_s_ls, n_a_ls, n_w_ls, total_step,
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model_config, seed=0):
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self.name = 'ia2c'
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self.agents = []
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self.n_agent = len(n_s_ls)
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self.reward_clip = model_config.getfloat('reward_clip')
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self.reward_norm = model_config.getfloat('reward_norm')
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self.n_s_ls = n_s_ls
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self.n_a_ls = n_a_ls
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self.n_w_ls = n_w_ls
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self.n_step = model_config.getint('batch_size')
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# init tf
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tf.reset_default_graph()
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tf.set_random_seed(seed)
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config = tf.ConfigProto(allow_soft_placement=True)
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self.sess = tf.Session(config=config)
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self.policy_ls = []
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for i, (n_s, n_w, n_a) in enumerate(zip(self.n_s_ls, self.n_w_ls, self.n_a_ls)):
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# agent_name is needed to differentiate multi-agents
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self.policy_ls.append(self._init_policy(n_s - n_w, n_a, n_w, 0, model_config,
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agent_name='{:d}a'.format(i)))
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self.saver = tf.train.Saver(max_to_keep=5)
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if total_step:
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# training
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self.total_step = total_step
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self._init_scheduler(model_config)
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self._init_train(model_config)
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self.sess.run(tf.global_variables_initializer())
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def _init_train(self, model_config):
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# init loss
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v_coef = model_config.getfloat('value_coef')
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max_grad_norm = model_config.getfloat('max_grad_norm')
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alpha = model_config.getfloat('rmsp_alpha')
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epsilon = model_config.getfloat('rmsp_epsilon')
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gamma = model_config.getfloat('gamma')
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self.trans_buffer_ls = []
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for i in range(self.n_agent):
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self.policy_ls[i].prepare_loss(
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v_coef, max_grad_norm, alpha, epsilon)
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self.trans_buffer_ls.append(OnPolicyBuffer(gamma))
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def backward(self, R_ls, summary_writer=None, global_step=None):
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cur_lr = self.lr_scheduler.get(self.n_step)
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cur_beta = self.beta_scheduler.get(self.n_step)
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for i in range(self.n_agent):
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obs, acts, dones, Rs, Advs = self.trans_buffer_ls[i].sample_transition(
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R_ls[i])
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# Check if len(mini_batch) = batch_size or not
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if len(obs) == self.n_step:
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if i == 0:
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self.policy_ls[i].backward(self.sess, obs, acts, dones, Rs, Advs, cur_lr, cur_beta,
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summary_writer=summary_writer, global_step=global_step)
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else:
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self.policy_ls[i].backward(
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self.sess, obs, acts, dones, Rs, Advs, cur_lr, cur_beta)
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def forward(self, obs, done, out_type='pv'):
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if len(out_type) == 1:
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out = []
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elif len(out_type) == 2:
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out1, out2 = [], []
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for i in range(self.n_agent):
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cur_out = self.policy_ls[i].forward(
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self.sess, obs[i], done, out_type)
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if len(out_type) == 1:
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out.append(cur_out)
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else:
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out1.append(cur_out[0])
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out2.append(cur_out[1])
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if len(out_type) == 1:
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return out
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else:
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return out1, out2
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def backward_mp(self, R_ls, summary_writer=None, global_step=None):
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cur_lr = self.lr_scheduler.get(self.n_step)
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cur_beta = self.beta_scheduler.get(self.n_step)
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def worker(i):
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obs, acts, dones, Rs, Advs = self.trans_buffer_ls[i].sample_transition(
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R_ls[i])
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self.policy_ls[i].backward(self.sess, obs, acts, dones, Rs, Advs, cur_lr, cur_beta,
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summary_writer=summary_writer, global_step=global_step)
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mps = []
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for i in range(self.n_agent):
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p = mp.Process(target=worker, args=(i))
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p.start()
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mps.append(p)
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for p in mps:
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p.join()
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def reset(self):
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for policy in self.policy_ls:
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policy._reset()
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def add_transition(self, obs, actions, rewards, values, done):
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if (self.reward_norm):
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rewards = rewards / self.reward_norm
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if self.reward_clip:
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rewards = np.clip(rewards, -self.reward_clip, self.reward_clip)
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for i in range(self.n_agent):
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self.trans_buffer_ls[i].add_transition(obs[i], actions[i],
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rewards[i], values[i], done)
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class MA2C(IA2C):
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def __init__(self, n_s_ls, n_a_ls, n_w_ls, n_f_ls, total_step,
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model_config, seed=0):
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self.name = 'ma2c'
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self.agents = []
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self.n_agent = len(n_s_ls)
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self.reward_clip = model_config.getfloat('reward_clip')
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self.reward_norm = model_config.getfloat('reward_norm')
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self.n_s_ls = n_s_ls
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self.n_a_ls = n_a_ls
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self.n_f_ls = n_f_ls
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self.n_w_ls = n_w_ls
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self.n_step = model_config.getint('batch_size')
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tf.reset_default_graph()
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tf.set_random_seed(seed)
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config = tf.ConfigProto(allow_soft_placement=True)
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self.sess = tf.Session(config=config)
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self.policy_ls = []
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for i, (n_s, n_a, n_w, n_f) in enumerate(zip(self.n_s_ls, self.n_a_ls, self.n_w_ls, self.n_f_ls)):
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# agent_name is needed to differentiate multi-agents
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self.policy_ls.append(self._init_policy(n_s - n_f - n_w, n_a, n_w, n_f, model_config,
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agent_name='{:d}a'.format(i)))
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self.saver = tf.train.Saver(max_to_keep=5)
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if total_step:
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# training
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self.total_step = total_step
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self._init_scheduler(model_config)
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self._init_train(model_config)
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self.sess.run(tf.global_variables_initializer())
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class IQL(A2C):
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def __init__(self, n_s_ls, n_a_ls, n_w_ls, total_step, model_config, seed=0, model_type='dqn'):
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self.name = 'iql'
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self.model_type = model_type
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self.agents = []
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self.n_agent = len(n_s_ls)
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self.reward_clip = model_config.getfloat('reward_clip')
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self.reward_norm = model_config.getfloat('reward_norm')
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self.n_s_ls = n_s_ls
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self.n_a_ls = n_a_ls
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self.n_w_ls = n_w_ls
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self.n_step = model_config.getint('batch_size')
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# init tf
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tf.reset_default_graph()
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tf.set_random_seed(seed)
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config = tf.ConfigProto(allow_soft_placement=True)
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self.sess = tf.Session(config=config)
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self.policy_ls = []
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for i, (n_s, n_a, n_w) in enumerate(zip(self.n_s_ls, self.n_a_ls, self.n_w_ls)):
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# agent_name is needed to differentiate multi-agents
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self.policy_ls.append(self._init_policy(n_s, n_a, n_w, model_config,
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agent_name='{:d}a'.format(i)))
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self.saver = tf.train.Saver(max_to_keep=5)
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if total_step:
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# training
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self.total_step = total_step
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self._init_scheduler(model_config)
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self._init_train(model_config)
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self.cur_step = 0
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self.sess.run(tf.global_variables_initializer())
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def _init_policy(self, n_s, n_a, n_w, model_config, agent_name=None):
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if self.model_type == 'dqn':
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n_h = model_config.getint('num_h')
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n_fc = model_config.getint('num_fc')
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policy = DeepQPolicy(n_s - n_w, n_a, n_w, self.n_step, n_fc0=n_fc, n_fc=n_h,
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name=agent_name)
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else:
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policy = LRQPolicy(n_s, n_a, self.n_step, name=agent_name)
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return policy
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def _init_scheduler(self, model_config):
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lr_init = model_config.getfloat('lr_init')
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lr_decay = model_config.get('lr_decay')
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eps_init = model_config.getfloat('epsilon_init')
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eps_decay = model_config.get('epsilon_decay')
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if lr_decay == 'constant':
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self.lr_scheduler = Scheduler(lr_init, decay=lr_decay)
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else:
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lr_min = model_config.getfloat('lr_min')
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self.lr_scheduler = Scheduler(
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lr_init, lr_min, self.total_step, decay=lr_decay)
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if eps_decay == 'constant':
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self.eps_scheduler = Scheduler(eps_init, decay=eps_decay)
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else:
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eps_min = model_config.getfloat('epsilon_min')
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eps_ratio = model_config.getfloat('epsilon_ratio')
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self.eps_scheduler = Scheduler(eps_init, eps_min, self.total_step * eps_ratio,
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decay=eps_decay)
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def _init_train(self, model_config):
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# init loss
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max_grad_norm = model_config.getfloat('max_grad_norm')
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gamma = model_config.getfloat('gamma')
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buffer_size = model_config.getfloat('buffer_size')
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self.trans_buffer_ls = []
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for i in range(self.n_agent):
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self.policy_ls[i].prepare_loss(max_grad_norm, gamma)
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self.trans_buffer_ls.append(ReplayBuffer(buffer_size, self.n_step))
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def backward(self, summary_writer=None, global_step=None):
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cur_lr = self.lr_scheduler.get(self.n_step)
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if self.trans_buffer_ls[0].size < self.trans_buffer_ls[0].batch_size:
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return
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for i in range(self.n_agent):
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for k in range(10):
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obs, acts, next_obs, rs, dones = self.trans_buffer_ls[i].sample_transition(
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)
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if i == 0:
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self.policy_ls[i].backward(self.sess, obs, acts, next_obs, dones, rs, cur_lr,
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summary_writer=summary_writer,
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global_step=global_step + k)
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else:
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self.policy_ls[i].backward(
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self.sess, obs, acts, next_obs, dones, rs, cur_lr)
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def forward(self, obs, mode='act', stochastic=False):
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if mode == 'explore':
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eps = self.eps_scheduler.get(1)
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action = []
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qs_ls = []
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for i in range(self.n_agent):
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qs = self.policy_ls[i].forward(self.sess, obs[i])
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if (mode == 'explore') and (np.random.random() < eps):
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action.append(np.random.randint(self.n_a_ls[i]))
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else:
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if not stochastic:
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action.append(np.argmax(qs))
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else:
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qs = qs / np.sum(qs)
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action.append(np.random.choice(np.arange(len(qs)), p=qs))
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qs_ls.append(qs)
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return action, qs_ls
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def reset(self):
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# do nothing
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return
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def add_transition(self, obs, actions, rewards, next_obs, done):
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if (self.reward_norm):
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rewards = rewards / self.reward_norm
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if self.reward_clip:
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rewards = np.clip(rewards, -self.reward_clip, self.reward_clip)
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for i in range(self.n_agent):
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self.trans_buffer_ls[i].add_transition(obs[i], actions[i],
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rewards[i], next_obs[i], done)
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@@ -0,0 +1,410 @@
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from agents.utils import *
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import numpy as np
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# import tensorflow
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# import tensorflow.compat.v1 as tf
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# tf.disable_v2_behavior()
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import tensorflow as tf
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tf.compat.v1.disable_v2_behavior()
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||||
class ACPolicy:
|
||||
def __init__(self, n_a, n_s, n_step, policy_name, agent_name):
|
||||
self.name = policy_name
|
||||
if agent_name is not None:
|
||||
# for multi-agent system
|
||||
self.name += '_' + str(agent_name)
|
||||
self.n_a = n_a
|
||||
self.n_s = n_s
|
||||
self.n_step = n_step
|
||||
|
||||
def forward(self, ob, *_args, **_kwargs):
|
||||
raise NotImplementedError()
|
||||
|
||||
def _build_out_net(self, h, out_type):
|
||||
if out_type == 'pi':
|
||||
pi = fc(h, out_type, self.n_a, act=tf.nn.softmax)
|
||||
return tf.squeeze(pi)
|
||||
else:
|
||||
v = fc(h, out_type, 1, act=lambda x: x)
|
||||
return tf.squeeze(v)
|
||||
|
||||
def _get_forward_outs(self, out_type):
|
||||
outs = []
|
||||
if 'p' in out_type:
|
||||
outs.append(self.pi)
|
||||
if 'v' in out_type:
|
||||
outs.append(self.v)
|
||||
return outs
|
||||
|
||||
def _return_forward_outs(self, out_values):
|
||||
if len(out_values) == 1:
|
||||
return out_values[0]
|
||||
return out_values
|
||||
|
||||
def prepare_loss(self, v_coef, max_grad_norm, alpha, epsilon):
|
||||
self.A = tf.compat.v1.placeholder(tf.int32, [self.n_step])
|
||||
self.ADV = tf.compat.v1.placeholder(tf.float32, [self.n_step])
|
||||
self.R = tf.compat.v1.placeholder(tf.float32, [self.n_step])
|
||||
self.entropy_coef = tf.compat.v1.placeholder(tf.float32, [])
|
||||
A_sparse = tf.one_hot(self.A, self.n_a)
|
||||
log_pi = tf.compat.v1.log(tf.clip_by_value(self.pi, 1e-10, 1.0))
|
||||
entropy = -tf.reduce_sum(self.pi * log_pi, axis=1)
|
||||
entropy_loss = -tf.reduce_mean(entropy) * self.entropy_coef
|
||||
policy_loss = - \
|
||||
tf.reduce_mean(tf.reduce_sum(log_pi * A_sparse, axis=1) * self.ADV)
|
||||
value_loss = tf.reduce_mean(tf.square(self.R - self.v)) * 0.5 * v_coef
|
||||
self.loss = policy_loss + value_loss + entropy_loss
|
||||
|
||||
wts = tf.compat.v1.trainable_variables(scope=self.name)
|
||||
grads = tf.compat.v1.gradients(self.loss, wts)
|
||||
if max_grad_norm > 0:
|
||||
grads, self.grad_norm = tf.clip_by_global_norm(
|
||||
grads, max_grad_norm)
|
||||
self.lr = tf.compat.v1.placeholder(tf.float32, [])
|
||||
self.optimizer = tf.compat.v1.train.RMSPropOptimizer(learning_rate=self.lr, decay=alpha,
|
||||
epsilon=epsilon)
|
||||
self._train = self.optimizer.apply_gradients(list(zip(grads, wts)))
|
||||
# monitor training
|
||||
if self.name.endswith('_0a'):
|
||||
summaries = []
|
||||
# summaries.append(tf.summary.scalar('loss/%s_entropy_loss' % self.name, entropy_loss))
|
||||
summaries.append(tf.compat.v1.summary.scalar(
|
||||
'loss/%s_policy_loss' % self.name, policy_loss))
|
||||
summaries.append(tf.compat.v1.summary.scalar(
|
||||
'loss/%s_value_loss' % self.name, value_loss))
|
||||
summaries.append(tf.compat.v1.summary.scalar(
|
||||
'loss/%s_total_loss' % self.name, self.loss))
|
||||
# summaries.append(tf.summary.scalar('train/%s_lr' % self.name, self.lr))
|
||||
# summaries.append(tf.summary.scalar('train/%s_entropy_beta' % self.name, self.entropy_coef))
|
||||
summaries.append(tf.compat.v1.summary.scalar(
|
||||
'train/%s_gradnorm' % self.name, self.grad_norm))
|
||||
self.summary = tf.compat.v1.summary.merge(summaries)
|
||||
|
||||
|
||||
class LstmACPolicy(ACPolicy):
|
||||
def __init__(self, n_s, n_a, n_w, n_step, n_fc_wave=128, n_fc_wait=32, n_lstm=64, name=None):
|
||||
super().__init__(n_a, n_s, n_step, 'lstm', name)
|
||||
self.n_lstm = n_lstm
|
||||
self.n_fc_wait = n_fc_wait
|
||||
self.n_fc_wave = n_fc_wave
|
||||
self.n_w = n_w
|
||||
self.ob_fw = tf.compat.v1.placeholder(
|
||||
tf.float32, [1, n_s + n_w]) # forward 1-step
|
||||
self.done_fw = tf.compat.v1.placeholder(tf.float32, [1])
|
||||
self.ob_bw = tf.compat.v1.placeholder(
|
||||
tf.float32, [n_step, n_s + n_w]) # backward n-step
|
||||
self.done_bw = tf.compat.v1.placeholder(tf.float32, [n_step])
|
||||
self.states = tf.compat.v1.placeholder(tf.float32, [2, n_lstm * 2])
|
||||
with tf.variable_scope(self.name):
|
||||
# pi and v use separate nets
|
||||
self.pi_fw, pi_state = self._build_net('forward', 'pi')
|
||||
self.v_fw, v_state = self._build_net('forward', 'v')
|
||||
pi_state = tf.expand_dims(pi_state, 0)
|
||||
v_state = tf.expand_dims(v_state, 0)
|
||||
self.new_states = tf.concat([pi_state, v_state], 0)
|
||||
with tf.variable_scope(self.name, reuse=True):
|
||||
self.pi, _ = self._build_net('backward', 'pi')
|
||||
self.v, _ = self._build_net('backward', 'v')
|
||||
self._reset()
|
||||
|
||||
def _build_net(self, in_type, out_type):
|
||||
if in_type == 'forward':
|
||||
ob = self.ob_fw
|
||||
done = self.done_fw
|
||||
else:
|
||||
ob = self.ob_bw
|
||||
done = self.done_bw
|
||||
if out_type == 'pi':
|
||||
states = self.states[0]
|
||||
else:
|
||||
states = self.states[1]
|
||||
if self.n_w == 0:
|
||||
h = fc(ob, out_type + '_fcw', self.n_fc_wave)
|
||||
else:
|
||||
h0 = fc(ob[:, :self.n_s], out_type + '_fcw', self.n_fc_wave)
|
||||
h1 = fc(ob[:, self.n_s:], out_type + '_fct', self.n_fc_wait)
|
||||
h = tf.concat([h0, h1], 1)
|
||||
h, new_states = lstm(h, done, states, out_type + '_lstm')
|
||||
out_val = self._build_out_net(h, out_type)
|
||||
return out_val, new_states
|
||||
|
||||
def _reset(self):
|
||||
# forget the cumulative states every cum_step
|
||||
self.states_fw = np.zeros((2, self.n_lstm * 2), dtype=np.float32)
|
||||
self.states_bw = np.zeros((2, self.n_lstm * 2), dtype=np.float32)
|
||||
|
||||
def forward(self, sess, ob, done, out_type='pv'):
|
||||
outs = self._get_forward_outs(out_type)
|
||||
# update state only when p is called
|
||||
if 'p' in out_type:
|
||||
outs.append(self.new_states)
|
||||
out_values = sess.run(outs, {self.ob_fw: np.array([ob]),
|
||||
self.done_fw: np.array([done]),
|
||||
self.states: self.states_fw})
|
||||
if 'p' in out_type:
|
||||
self.states_fw = out_values[-1]
|
||||
out_values = out_values[:-1]
|
||||
return self._return_forward_outs(out_values)
|
||||
|
||||
def backward(self, sess, obs, acts, dones, Rs, Advs, cur_lr, cur_beta,
|
||||
summary_writer=None, global_step=None):
|
||||
if summary_writer is None:
|
||||
ops = self._train
|
||||
else:
|
||||
ops = [self.summary, self._train]
|
||||
outs = sess.run(ops,
|
||||
{self.ob_bw: obs,
|
||||
self.done_bw: dones,
|
||||
self.states: self.states_bw,
|
||||
self.A: acts,
|
||||
self.ADV: Advs,
|
||||
self.R: Rs,
|
||||
self.lr: cur_lr,
|
||||
self.entropy_coef: cur_beta})
|
||||
self.states_bw = np.copy(self.states_fw)
|
||||
if summary_writer is not None:
|
||||
summary_writer.add_summary(outs[0], global_step=global_step)
|
||||
|
||||
def _get_forward_outs(self, out_type):
|
||||
outs = []
|
||||
if 'p' in out_type:
|
||||
outs.append(self.pi_fw)
|
||||
if 'v' in out_type:
|
||||
outs.append(self.v_fw)
|
||||
return outs
|
||||
|
||||
|
||||
class FPLstmACPolicy(LstmACPolicy):
|
||||
def __init__(self, n_s, n_a, n_w, n_f, n_step, n_fc_wave=128, n_fc_wait=32, n_fc_fp=32, n_lstm=64, name=None):
|
||||
ACPolicy.__init__(self, n_a, n_s, n_step, 'fplstm', name)
|
||||
self.n_lstm = n_lstm
|
||||
self.n_fc_wave = n_fc_wave
|
||||
self.n_fc_wait = n_fc_wait
|
||||
self.n_fc_fp = n_fc_fp
|
||||
self.n_w = n_w
|
||||
self.ob_fw = tf.compat.v1.placeholder(
|
||||
tf.float32, [1, n_s + n_w + n_f]) # forward 1-step
|
||||
self.done_fw = tf.compat.v1.placeholder(tf.float32, [1])
|
||||
self.ob_bw = tf.compat.v1.placeholder(
|
||||
tf.float32, [n_step, n_s + n_w + n_f]) # backward n-step
|
||||
self.done_bw = tf.compat.v1.placeholder(tf.float32, [n_step])
|
||||
self.states = tf.compat.v1.placeholder(tf.float32, [2, n_lstm * 2])
|
||||
with tf.compat.v1.variable_scope(self.name):
|
||||
# pi and v use separate nets
|
||||
self.pi_fw, pi_state = self._build_net('forward', 'pi')
|
||||
self.v_fw, v_state = self._build_net('forward', 'v')
|
||||
pi_state = tf.expand_dims(pi_state, 0)
|
||||
v_state = tf.expand_dims(v_state, 0)
|
||||
self.new_states = tf.concat([pi_state, v_state], 0)
|
||||
with tf.compat.v1.variable_scope(self.name, reuse=True):
|
||||
self.pi, _ = self._build_net('backward', 'pi')
|
||||
self.v, _ = self._build_net('backward', 'v')
|
||||
self._reset()
|
||||
|
||||
def _build_net(self, in_type, out_type):
|
||||
if in_type == 'forward':
|
||||
ob = self.ob_fw
|
||||
done = self.done_fw
|
||||
else:
|
||||
ob = self.ob_bw
|
||||
done = self.done_bw
|
||||
if out_type == 'pi':
|
||||
states = self.states[0]
|
||||
else:
|
||||
states = self.states[1]
|
||||
h0 = fc(ob[:, :self.n_s], out_type + '_fcw', self.n_fc_wave)
|
||||
h1 = fc(ob[:, (self.n_s + self.n_w):], out_type + '_fcf', self.n_fc_fp)
|
||||
if self.n_w == 0:
|
||||
h = tf.concat([h0, h1], 1)
|
||||
else:
|
||||
h2 = fc(ob[:, self.n_s: (self.n_s + self.n_w)],
|
||||
out_type + '_fct', self.n_fc_wait)
|
||||
h = tf.concat([h0, h1, h2], 1)
|
||||
h, new_states = lstm(h, done, states, out_type + '_lstm')
|
||||
out_val = self._build_out_net(h, out_type)
|
||||
return out_val, new_states
|
||||
|
||||
|
||||
class FcACPolicy(ACPolicy):
|
||||
def __init__(self, n_s, n_a, n_w, n_step, n_fc_wave=128, n_fc_wait=32, n_lstm=64, name=None):
|
||||
super().__init__(n_a, n_s, n_step, 'fc', name)
|
||||
self.n_fc_wave = n_fc_wave
|
||||
self.n_fc_wait = n_fc_wait
|
||||
self.n_fc = n_lstm
|
||||
self.n_w = n_w
|
||||
self.obs = tf.placeholder(tf.float32, [None, n_s + n_w])
|
||||
with tf.variable_scope(self.name):
|
||||
# pi and v use separate nets
|
||||
self.pi = self._build_net('pi')
|
||||
self.v = self._build_net('v')
|
||||
|
||||
def _build_net(self, out_type):
|
||||
if self.n_w == 0:
|
||||
h = fc(self.obs, out_type + '_fcw', self.n_fc_wave)
|
||||
else:
|
||||
h0 = fc(self.obs[:, :self.n_s], out_type + '_fcw', self.n_fc_wave)
|
||||
h1 = fc(self.obs[:, self.n_s:], out_type + '_fct', self.n_fc_wait)
|
||||
h = tf.concat([h0, h1], 1)
|
||||
h = fc(h, out_type + '_fc', self.n_fc)
|
||||
return self._build_out_net(h, out_type)
|
||||
|
||||
def forward(self, sess, ob, done, out_type='pv'):
|
||||
outs = self._get_forward_outs(out_type)
|
||||
out_values = sess.run(outs, {self.obs: np.array([ob])})
|
||||
return self._return_forward_outs(out_values)
|
||||
|
||||
def backward(self, sess, obs, acts, dones, Rs, Advs, cur_lr, cur_beta,
|
||||
summary_writer=None, global_step=None):
|
||||
if summary_writer is None:
|
||||
ops = self._train
|
||||
else:
|
||||
ops = [self.summary, self._train]
|
||||
outs = sess.run(ops,
|
||||
{self.obs: obs,
|
||||
self.A: acts,
|
||||
self.ADV: Advs,
|
||||
self.R: Rs,
|
||||
self.lr: cur_lr,
|
||||
self.entropy_coef: cur_beta})
|
||||
if summary_writer is not None:
|
||||
summary_writer.add_summary(outs[0], global_step=global_step)
|
||||
|
||||
|
||||
class FPFcACPolicy(FcACPolicy):
|
||||
def __init__(self, n_s, n_a, n_w, n_f, n_step, n_fc_wave=128, n_fc_wait=32, n_fc_fp=32, n_lstm=64, name=None):
|
||||
ACPolicy.__init__(self, n_a, n_s, n_step, 'fpfc', name)
|
||||
self.n_fc_wave = n_fc_wave
|
||||
self.n_fc_wait = n_fc_wait
|
||||
self.n_fc_fp = n_fc_fp
|
||||
self.n_fc = n_lstm
|
||||
self.n_w = n_w
|
||||
self.obs = tf.placeholder(tf.float32, [None, n_s + n_w + n_f])
|
||||
with tf.variable_scope(self.name):
|
||||
# pi and v use separate nets
|
||||
self.pi = self._build_net('pi')
|
||||
self.v = self._build_net('v')
|
||||
|
||||
def _build_net(self, out_type):
|
||||
h0 = fc(ob[:, :self.n_s], out_type + '_fcw', self.n_fc_wave)
|
||||
h1 = fc(ob[:, (self.n_s + self.n_w):], out_type + '_fcf', self.n_fc_fp)
|
||||
if self.n_w == 0:
|
||||
h = tf.concat([h0, h1], 1)
|
||||
else:
|
||||
h2 = fc(ob[:, self.n_s: (self.n_s + self.n_w)],
|
||||
out_type + '_fct', self.n_fc_wait)
|
||||
h = tf.concat([h0, h1, h2], 1)
|
||||
h = fc(h, out_type + '_fc', self.n_fc)
|
||||
return self._build_out_net(h, out_type)
|
||||
|
||||
|
||||
class QPolicy:
|
||||
def __init__(self, n_a, n_s, n_step, policy_name, agent_name):
|
||||
self.name = policy_name
|
||||
if agent_name is not None:
|
||||
# for multi-agent system
|
||||
self.name += '_' + str(agent_name)
|
||||
self.n_a = n_a
|
||||
self.n_s = n_s
|
||||
self.n_step = n_step
|
||||
|
||||
def forward(self, ob, *_args, **_kwargs):
|
||||
raise NotImplementedError()
|
||||
|
||||
def _build_fc_net(self, h, n_fc_ls):
|
||||
for i, n_fc in enumerate(n_fc_ls):
|
||||
h = fc(h, 'q_fc_%d' % i, n_fc)
|
||||
q = fc(h, 'q', self.n_a, act=lambda x: x)
|
||||
return tf.squeeze(q)
|
||||
|
||||
def _build_net(self):
|
||||
raise NotImplementedError()
|
||||
|
||||
def prepare_loss(self, max_grad_norm, gamma):
|
||||
self.A = tf.placeholder(tf.int32, [self.n_step])
|
||||
self.S1 = tf.placeholder(
|
||||
tf.float32, [self.n_step, self.n_s + self.n_w])
|
||||
self.R = tf.placeholder(tf.float32, [self.n_step])
|
||||
self.DONE = tf.placeholder(tf.bool, [self.n_step])
|
||||
A_sparse = tf.one_hot(self.A, self.n_a)
|
||||
|
||||
# backward
|
||||
with tf.variable_scope(self.name + '_q', reuse=True):
|
||||
q0s = self._build_net(self.S)
|
||||
q0 = tf.reduce_sum(q0s * A_sparse, axis=1)
|
||||
with tf.variable_scope(self.name + '_q', reuse=True):
|
||||
q1s = self._build_net(self.S1)
|
||||
q1 = tf.reduce_max(q1s, axis=1)
|
||||
tq = tf.stop_gradient(tf.where(self.DONE, self.R, self.R + gamma * q1))
|
||||
self.loss = tf.reduce_mean(tf.square(q0 - tq))
|
||||
|
||||
wts = tf.trainable_variables(scope=self.name)
|
||||
grads = tf.gradients(self.loss, wts)
|
||||
if max_grad_norm > 0:
|
||||
grads, self.grad_norm = tf.clip_by_global_norm(
|
||||
grads, max_grad_norm)
|
||||
self.lr = tf.placeholder(tf.float32, [])
|
||||
self.optimizer = tf.train.AdamOptimizer(learning_rate=self.lr)
|
||||
self._train = self.optimizer.apply_gradients(list(zip(grads, wts)))
|
||||
# monitor training
|
||||
if self.name.endswith('_0a'):
|
||||
summaries = []
|
||||
summaries.append(tf.summary.scalar(
|
||||
'train/%s_loss' % self.name, self.loss))
|
||||
summaries.append(tf.summary.scalar('train/%s_q' %
|
||||
self.name, tf.reduce_mean(q0)))
|
||||
summaries.append(tf.summary.scalar('train/%s_tq' %
|
||||
self.name, tf.reduce_mean(tq)))
|
||||
summaries.append(tf.summary.scalar(
|
||||
'train/%s_gradnorm' % self.name, self.grad_norm))
|
||||
self.summary = tf.summary.merge(summaries)
|
||||
|
||||
|
||||
class DeepQPolicy(QPolicy):
|
||||
def __init__(self, n_s, n_a, n_w, n_step, n_fc0=128, n_fc=64, name=None):
|
||||
super().__init__(n_a, n_s, n_step, 'dqn', name)
|
||||
self.n_fc = n_fc
|
||||
self.n_fc0 = n_fc0
|
||||
self.n_w = n_w
|
||||
self.S = tf.placeholder(tf.float32, [None, n_s + n_w])
|
||||
with tf.variable_scope(self.name + '_q'):
|
||||
self.qvalues = self._build_net(self.S)
|
||||
|
||||
def _build_net(self, S):
|
||||
if self.n_w == 0:
|
||||
h = fc(S, 'q_fcw', self.n_fc0)
|
||||
else:
|
||||
h0 = fc(S[:, :self.n_s], 'q_fcw', self.n_fc0)
|
||||
h1 = fc(S[:, self.n_s:], 'q_fct', self.n_fc0 / 4)
|
||||
h = tf.concat([h0, h1], 1)
|
||||
return self._build_fc_net(h, [self.n_fc])
|
||||
|
||||
def forward(self, sess, ob):
|
||||
return sess.run(self.qvalues, {self.S: np.array([ob])})
|
||||
|
||||
def backward(self, sess, obs, acts, next_obs, dones, rs, cur_lr,
|
||||
summary_writer=None, global_step=None):
|
||||
if summary_writer is None:
|
||||
ops = self._train
|
||||
else:
|
||||
ops = [self.summary, self._train]
|
||||
outs = sess.run(ops,
|
||||
{self.S: obs,
|
||||
self.A: acts,
|
||||
self.S1: next_obs,
|
||||
self.DONE: dones,
|
||||
self.R: rs,
|
||||
self.lr: cur_lr})
|
||||
if summary_writer is not None:
|
||||
summary_writer.add_summary(outs[0], global_step=global_step)
|
||||
|
||||
|
||||
class LRQPolicy(DeepQPolicy):
|
||||
def __init__(self, n_s, n_a, n_step, name=None):
|
||||
QPolicy.__init__(self, n_a, n_s, n_step, 'lr', name)
|
||||
self.S = tf.compat.v1.placeholder(tf.float32, [None, n_s])
|
||||
self.n_w = 0
|
||||
with tf.compat.v1.variable_scope(self.name + '_q'):
|
||||
self.qvalues = self._build_net(self.S)
|
||||
|
||||
def _build_net(self, S):
|
||||
return self._build_fc_net(S, [])
|
||||
+300
@@ -0,0 +1,300 @@
|
||||
import numpy as np
|
||||
import random
|
||||
import tensorflow as tf
|
||||
|
||||
"""
|
||||
initializers
|
||||
"""
|
||||
DEFAULT_SCALE = np.sqrt(2)
|
||||
DEFAULT_MODE = 'fan_in'
|
||||
|
||||
|
||||
def ortho_init(scale=DEFAULT_SCALE, mode=None):
|
||||
def _ortho_init(shape, dtype, partition_info=None):
|
||||
# lasagne ortho init for tf
|
||||
shape = tuple(shape)
|
||||
if len(shape) == 2: # fc: in, out
|
||||
flat_shape = shape
|
||||
elif (len(shape) == 3) or (len(shape) == 4): # 1d/2dcnn: (in_h), in_w, in_c, out
|
||||
flat_shape = (np.prod(shape[:-1]), shape[-1])
|
||||
a = np.random.standard_normal(flat_shape)
|
||||
u, _, v = np.linalg.svd(a, full_matrices=False)
|
||||
q = u if u.shape == flat_shape else v # pick the one with the correct shape
|
||||
q = q.reshape(shape)
|
||||
return (scale * q).astype(np.float32)
|
||||
return _ortho_init
|
||||
|
||||
|
||||
def norm_init(scale=DEFAULT_SCALE, mode=DEFAULT_MODE):
|
||||
def _norm_init(shape, dtype, partition_info=None):
|
||||
shape = tuple(shape)
|
||||
if len(shape) == 2:
|
||||
n_in = shape[0]
|
||||
elif (len(shape) == 3) or (len(shape) == 4):
|
||||
n_in = np.prod(shape[:-1])
|
||||
a = np.random.standard_normal(shape)
|
||||
if mode == 'fan_in':
|
||||
n = n_in
|
||||
elif mode == 'fan_out':
|
||||
n = shape[-1]
|
||||
elif mode == 'fan_avg':
|
||||
n = 0.5 * (n_in + shape[-1])
|
||||
return (scale * a / np.sqrt(n)).astype(np.float32)
|
||||
|
||||
|
||||
DEFAULT_METHOD = ortho_init
|
||||
"""
|
||||
layers
|
||||
"""
|
||||
|
||||
|
||||
def conv(x, scope, n_out, f_size, stride=1, pad='VALID', f_size_w=None, act=tf.nn.relu,
|
||||
conv_dim=1, init_scale=DEFAULT_SCALE, init_mode=None, init_method=DEFAULT_METHOD):
|
||||
with tf.variable_scope(scope):
|
||||
b = tf.get_variable(
|
||||
"b", [n_out], initializer=tf.constant_initializer(0.0))
|
||||
if conv_dim == 1:
|
||||
n_c = x.shape[2].value
|
||||
w = tf.get_variable("w", [f_size, n_c, n_out],
|
||||
initializer=init_method(init_scale, init_mode))
|
||||
z = tf.nn.conv1d(x, w, stride=stride, padding=pad) + b
|
||||
elif conv_dim == 2:
|
||||
n_c = x.shape[3].value
|
||||
if f_size_w is None:
|
||||
f_size_w = f_size
|
||||
w = tf.get_variable("w", [f_size, f_size_w, n_c, n_out],
|
||||
initializer=init_method(init_scale, init_mode))
|
||||
z = tf.nn.conv2d(
|
||||
x, w, strides=[1, stride, stride, 1], padding=pad) + b
|
||||
return act(z)
|
||||
|
||||
|
||||
def fc(x, scope, n_out, act=tf.nn.relu, init_scale=DEFAULT_SCALE,
|
||||
init_mode=DEFAULT_MODE, init_method=DEFAULT_METHOD):
|
||||
with tf.compat.v1.variable_scope(scope):
|
||||
n_in = x.shape[1].value
|
||||
w = tf.compat.v1.get_variable("w", [n_in, n_out],
|
||||
initializer=init_method(init_scale, init_mode))
|
||||
b = tf.compat.v1.get_variable(
|
||||
"b", [n_out], initializer=tf.constant_initializer(0.0))
|
||||
z = tf.matmul(x, w) + b
|
||||
return act(z)
|
||||
|
||||
|
||||
def batch_to_seq(x):
|
||||
n_step = x.shape[0].value
|
||||
if len(x.shape) == 1:
|
||||
x = tf.expand_dims(x, -1)
|
||||
return tf.split(axis=0, num_or_size_splits=n_step, value=x)
|
||||
|
||||
|
||||
def seq_to_batch(x):
|
||||
return tf.concat(axis=0, values=x)
|
||||
|
||||
|
||||
def lstm(xs, dones, s, scope, init_scale=DEFAULT_SCALE, init_mode=DEFAULT_MODE,
|
||||
init_method=DEFAULT_METHOD):
|
||||
xs = batch_to_seq(xs)
|
||||
# need dones to reset states
|
||||
dones = batch_to_seq(dones)
|
||||
n_in = xs[0].shape[1].value
|
||||
n_out = s.shape[0] // 2
|
||||
with tf.compat.v1.variable_scope(scope):
|
||||
wx = tf.compat.v1.get_variable("wx", [n_in, n_out*4],
|
||||
initializer=init_method(init_scale, init_mode))
|
||||
wh = tf.compat.v1.get_variable("wh", [n_out, n_out*4],
|
||||
initializer=init_method(init_scale, init_mode))
|
||||
b = tf.compat.v1.get_variable(
|
||||
"b", [n_out*4], initializer=tf.constant_initializer(0.0))
|
||||
s = tf.expand_dims(s, 0)
|
||||
c, h = tf.split(axis=1, num_or_size_splits=2, value=s)
|
||||
for ind, (x, done) in enumerate(zip(xs, dones)):
|
||||
c = c * (1-done)
|
||||
h = h * (1-done)
|
||||
z = tf.matmul(x, wx) + tf.matmul(h, wh) + b
|
||||
i, f, o, u = tf.split(axis=1, num_or_size_splits=4, value=z)
|
||||
i = tf.nn.sigmoid(i)
|
||||
f = tf.nn.sigmoid(f)
|
||||
o = tf.nn.sigmoid(o)
|
||||
u = tf.tanh(u)
|
||||
c = f*c + i*u
|
||||
h = o*tf.tanh(c)
|
||||
xs[ind] = h
|
||||
s = tf.concat(axis=1, values=[c, h])
|
||||
return seq_to_batch(xs), tf.squeeze(s)
|
||||
|
||||
|
||||
def test_layers():
|
||||
print(tf.__version__)
|
||||
tf.reset_default_graph()
|
||||
sess = tf.Session()
|
||||
n_step = 5
|
||||
fc_x = tf.placeholder(tf.float32, [None, 10])
|
||||
lstm_x = tf.placeholder(tf.float32, [n_step, 2])
|
||||
lstm_done = tf.placeholder(tf.float32, [n_step])
|
||||
lstm_s = tf.placeholder(tf.float32, [20])
|
||||
conv1_x = tf.placeholder(tf.float32, [None, 8, 1])
|
||||
conv2_x = tf.placeholder(tf.float32, [None, 8, 8, 1])
|
||||
fc_out = fc(fc_x, 'fc', 10)
|
||||
lstm_out, lstm_ns = lstm(lstm_x, lstm_done, lstm_s, 'lstm')
|
||||
conv1_out = conv(conv1_x, 'conv1', 10, 4, conv_dim=1)
|
||||
conv2_out = conv(conv2_x, 'conv2', 10, 4, conv_dim=2)
|
||||
sess.run(tf.global_variables_initializer())
|
||||
inputs = {'fc': {fc_x: np.random.randn(n_step, 10)},
|
||||
'lstm_done': {lstm_x: np.zeros((n_step, 2)),
|
||||
lstm_done: np.ones(n_step),
|
||||
lstm_s: np.random.randn(20)},
|
||||
'lstm': {lstm_x: np.random.randn(n_step, 2),
|
||||
lstm_done: np.zeros(n_step),
|
||||
lstm_s: np.random.randn(20)},
|
||||
'conv1': {conv1_x: np.random.randn(n_step, 8, 1)},
|
||||
'conv2': {conv2_x: np.random.randn(n_step, 8, 8, 1)}}
|
||||
outputs = {'fc': [fc_out], 'lstm_done': [lstm_out, lstm_ns],
|
||||
'conv1': [conv1_out], 'conv2': [conv2_out],
|
||||
'lstm': [lstm_out, lstm_ns]}
|
||||
for scope in ['fc', 'lstm', 'conv1', 'conv2']:
|
||||
print(scope)
|
||||
wts = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES, scope=scope)
|
||||
for wt in wts:
|
||||
wt_val = wt.eval(sess)
|
||||
print(wt_val.shape)
|
||||
print(np.mean(wt_val), np.std(wt_val),
|
||||
np.min(wt_val), np.max(wt_val))
|
||||
print('=====================================')
|
||||
for x_name in inputs:
|
||||
print(x_name)
|
||||
out = sess.run(outputs[x_name], inputs[x_name])
|
||||
if x_name.startswith('lstm'):
|
||||
print(out[0])
|
||||
print(out[1])
|
||||
else:
|
||||
print(out[0].shape)
|
||||
|
||||
|
||||
"""
|
||||
buffers
|
||||
"""
|
||||
|
||||
|
||||
class TransBuffer:
|
||||
def reset(self):
|
||||
self.buffer = []
|
||||
|
||||
@property
|
||||
def size(self):
|
||||
return len(self.buffer)
|
||||
|
||||
def add_transition(self, ob, a, r, *_args, **_kwargs):
|
||||
raise NotImplementedError()
|
||||
|
||||
def sample_transition(self, *_args, **_kwargs):
|
||||
raise NotImplementedError()
|
||||
|
||||
|
||||
class OnPolicyBuffer(TransBuffer):
|
||||
def __init__(self, gamma):
|
||||
self.gamma = gamma
|
||||
self.reset()
|
||||
|
||||
def reset(self, done=False):
|
||||
# the done before each step is required
|
||||
self.obs = []
|
||||
self.acts = []
|
||||
self.rs = []
|
||||
self.vs = []
|
||||
self.dones = [done]
|
||||
|
||||
def add_transition(self, ob, a, r, v, done):
|
||||
self.obs.append(ob)
|
||||
self.acts.append(a)
|
||||
self.rs.append(r)
|
||||
self.vs.append(v)
|
||||
self.dones.append(done)
|
||||
|
||||
def _add_R_Adv(self, R):
|
||||
Rs = []
|
||||
Advs = []
|
||||
# use post-step dones here
|
||||
for r, v, done in zip(self.rs[::-1], self.vs[::-1], self.dones[:0:-1]):
|
||||
R = r + self.gamma * R * (1.-done)
|
||||
Adv = R - v
|
||||
Rs.append(R)
|
||||
Advs.append(Adv)
|
||||
Rs.reverse()
|
||||
Advs.reverse()
|
||||
self.Rs = Rs
|
||||
self.Advs = Advs
|
||||
|
||||
def sample_transition(self, R, discrete=True):
|
||||
self._add_R_Adv(R)
|
||||
obs = np.array(self.obs, dtype=np.float32)
|
||||
if discrete:
|
||||
acts = np.array(self.acts, dtype=np.int32)
|
||||
else:
|
||||
acts = np.array(self.acts, dtype=np.float32)
|
||||
Rs = np.array(self.Rs, dtype=np.float32)
|
||||
Advs = np.array(self.Advs, dtype=np.float32)
|
||||
# use pre-step dones here
|
||||
dones = np.array(self.dones[:-1], dtype=np.bool)
|
||||
self.reset(self.dones[-1])
|
||||
return obs, acts, dones, Rs, Advs
|
||||
|
||||
|
||||
class ReplayBuffer(TransBuffer):
|
||||
def __init__(self, buffer_size, batch_size):
|
||||
self.buffer_size = buffer_size
|
||||
self.batch_size = batch_size
|
||||
self.cum_size = 0
|
||||
self.buffer = []
|
||||
|
||||
def add_transition(self, ob, a, r, next_ob, done):
|
||||
experience = (ob, a, r, next_ob, done)
|
||||
if self.cum_size < self.buffer_size:
|
||||
self.buffer.append(experience)
|
||||
else:
|
||||
ind = int(self.cum_size % self.buffer_size)
|
||||
self.buffer[ind] = experience
|
||||
self.cum_size += 1
|
||||
|
||||
def reset(self):
|
||||
self.buffer = []
|
||||
self.cum_size = 0
|
||||
|
||||
def sample_transition(self):
|
||||
# Randomly sample batch_size examples
|
||||
minibatch = random.sample(self.buffer, self.batch_size)
|
||||
state_batch = np.asarray([data[0] for data in minibatch])
|
||||
action_batch = np.asarray([data[1] for data in minibatch])
|
||||
next_state_batch = np.asarray([data[3] for data in minibatch])
|
||||
reward_batch = np.asarray([data[2] for data in minibatch])
|
||||
done_batch = np.asarray([data[4] for data in minibatch])
|
||||
return state_batch, action_batch, next_state_batch, reward_batch, done_batch
|
||||
|
||||
@property
|
||||
def size(self):
|
||||
return min(self.buffer_size, self.cum_size)
|
||||
|
||||
|
||||
"""
|
||||
util functions
|
||||
"""
|
||||
|
||||
|
||||
class Scheduler:
|
||||
def __init__(self, val_init, val_min=0, total_step=0, decay='linear'):
|
||||
self.val = val_init
|
||||
self.N = float(total_step)
|
||||
self.val_min = val_min
|
||||
self.decay = decay
|
||||
self.n = 0
|
||||
|
||||
def get(self, n_step):
|
||||
self.n += n_step
|
||||
if self.decay == 'linear':
|
||||
return max(self.val_min, self.val * (1 - self.n / self.N))
|
||||
else:
|
||||
return self.val
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
test_layers()
|
||||
Reference in New Issue
Block a user