67 lines
2.6 KiB
Markdown
67 lines
2.6 KiB
Markdown
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# Mean Field Games Tutorial (Production)
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This page summarizes the full MFG tutorial notebook (`examples/notebooks/mean_field_games_tutorial.ipynb`) and the accompanying audit in `docs/MFG_TUTORIAL_COMPLETE.md`.
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## What the notebook demonstrates
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- Rust-backed 1D MFG solver (`solve_mfg_1d_rust`) with PyO3 bindings
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- Coupled HJB–Fokker-Planck fixed-point iteration with congestion term
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- Execution time: ~0.4 s for a 100×100 grid (agents × time)
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- Stable mass conservation and no NaNs across iterations
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- Visual outputs: convergence plot, 3D density evolution, 3D value surface, time-slice snapshots
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## Problem setup
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- Spatial grid: $x \in [0, 1]$, 100 points; time grid: 100 steps, $T = 1.0$
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- Viscosity $\nu = 0.01$, relaxation $\alpha = 0.5$, congestion penalty $\lambda = 0.5$
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- Initial distribution $m_0$: Gaussian centered at $x=0.3$
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- Terminal cost $u_T(x) = 0.5(x - 0.7)^2$ (agents target $x=0.7$)
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### Core equations
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.. math::
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-\partial_t u - \nu\,\partial_{xx} u + H\big(x, \partial_x u, m\big) = 0,\\
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\partial_t m - \nu\,\partial_{xx} m - \operatorname{div}\big(m\, \partial_p H\big) = 0.
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We iterate between backward $u$ and forward $m$ with mass renormalization to keep $\int m \, dx = 1$.
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## Usage snippet
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```python
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import numpy as np
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from optimizr import MFGConfig, solve_mfg_1d_rust
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x = np.linspace(0, 1, 100)
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m0 = np.exp(-50 * (x - 0.3) ** 2)
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m0 /= np.trapz(m0, x)
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u_terminal = 0.5 * (x - 0.7) ** 2
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config = MFGConfig(nx=100, nt=100, x_min=0.0, x_max=1.0, T=1.0, nu=0.01, max_iter=50, tol=1e-5, alpha=0.5)
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u, m, iters = solve_mfg_1d_rust(m0, u_terminal, config, lambda_congestion=0.5)
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print(f"converged in {iters} iterations: u{u.shape}, m{m.shape}")
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```
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## Key observations
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- Agents split and migrate toward the target region; congestion prevents collapse into a single spike.
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- Value function decreases smoothly over time, capturing optimal cost-to-go.
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- Convergence is monotone in practice; fixed-point loop hits tolerance within ~50 iterations.
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## Why the Rust backend matters
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- Implicit diffusion step and upwind transport improve stability over the reference Python solver.
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- Rayon parallelism speeds up 2D grids; OpenBLAS accelerates dense linear algebra where applicable.
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- Safe bindings via PyO3 with abi3 wheels keep installation friction low.
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## Reproducing visuals
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- Run the notebook end-to-end to generate 3D surfaces and time-slice plots.
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- Export figures from the notebook if you need static assets for papers or presentations.
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## Next steps (tracked)
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- Add 2D MFG example with separable costs.
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- Extend congestion models (e.g., polynomial costs) and compare convergence rates.
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- Log convergence metrics to CSV for batch sweeps.
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