This repository has all the codes and sources of various RL algorithms that I have implemented.

A collection of deep and tabular reinforcement learning algorithms, including implementations of DQN, Dueling DQN, Q-learning, SARSA, n-step Tree Backup, and Monte Carlo.

Q-Learning Agent that Learns and Plays Alan Parr’s Traffic Lights Game

Benchmarking 11 tabular RL methods on a 6×6 FrozenLake grid under deterministic and slippery dynamics.

Implementation of Q-Learning, SARSA, and Dyna Q to allow an agent to navigate the FrozenLake-v0 environment from OpenAI.

RL was cheaper. The heuristic was safer. Neither was correct. POLARIS stress-tests operational policies under chaos, demand spikes, and black swan events asking one question: which policy survives when everything goes wrong? Built with constrained RL, Bayesian modeling, CVaR risk metrics, and a human-in-the-loop governor.

OpenSpiel is a collection of environments and algorithms for research in general reinforcement learning and search/planning in games.

Reinforcement learning for reactor control under partial observability using Q-learning and SARSA(λ), with analysis of noise robustness and policy behavior.

Matchbox-RL: A tangible reinforcement learning library for Python. Based on Donald Michie's 1961 MENACE algorithm using matchboxes and colored beads. Perfect for education and visualizing RL.

强化学习中文学习笔记与可复现实验仓库，涵盖 Q-Learning、SARSA、Monte Carlo Control 及 FrozenLake、CliffWalking、Blackjack 示例。