sutton-barto

RL study guide — foundations through RLHF, DPO, GRPO, RLVR, agentic RL, and offline RL. Hand-written CS294 notes, 19 lecture drafts, 5 tested exercises, citations that resolve.

Reinforcement learning algorithms with mathematical derivations and Sutton & Barto figure reproductions.

Sutton & Barto'nun Reinforcement Learning: An Introduction kitabi uzerine ozgun Turkce calisma notlari + mobil uyumlu web okuyucu

🎯 Reinforcement Learning portfolio · 5 envs · 20+ algorithms (DP, MC, TD, SARSA, Q-Learning, REINFORCE, MCTS) + Deep RL (DQN, AlphaZero) · 71 pytest tests

Deep Reinforcement Learning from mathematical foundations to PyTorch implementations: rigorous proofs, step-by-step derivations of objectives and gradient estimators, and reproducible experiments on Gymnasium and MuJoCo benchmarks spanning policy gradients, actor-critic methods, value-based learning, and continuous control.

Sutton and Barto's 10-armed bandit testbed with epsilon-greedy, optimistic initialization, and UCB, averaged over 2,000 problems.

Reinforcement Learning: Gymnasium/PettingZoo (Farama), Sutton & Barto, HuggingFace RL — session material for the DS/ML Collaborative Learning Group

Interactive RL learning platform: 13 chapters from Sutton & Barto, 18K+ lines, fill-in-the-blank exercises with bilingual explanations. Bandits → DP → MC → TD → Policy Gradient → DQN → PPO → SAC → MARL → RLHF

Every algorithm from Sutton & Barto Ch 1-13 - tabular RL in NumPy, deep RL in PyTorch

Fork of ShangtongZhang/reinforcement-learning-an-introduction - Python implementations of algorithms from Sutton and Barto's RL textbook (2nd Edition)

n-armed bandit algorithms comparison + simulation app

Windy Gridworld with Q-learning, SARSA, and Expected SARSA, including learning curves and the trained agent's path.