A comprehensive research framework for Temporal Difference learning, implementing 9 state-of-the-art algorithms with theoretical analysis, empirical evaluation, and interactive visualization. Features custom environments, convergence proofs, bias-variance tradeoff analysis, and reproducible experimental pipelines for reinforcement learning research
benchmarking reinforcement-learning q-learning sarsa-learning stochastic-approximation bias-variance-tradeoff markov-decision-process tabular-rl bellmann-equation temporal-diff value-based-care
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Updated
Aug 3, 2026 - Python