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Title: GitHub - srikanthbaride/Reinforcement-Learning-Explained-Code: Companion Python code for the book "Reinforcement Learning Explained" · GitHub

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Description: Companion Python code for the book "Reinforcement Learning Explained" - srikanthbaride/Reinforcement-Learning-Explained-Code

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https://github.com/srikanthbaride/Reinforcement-Learning-Explained-Code#-chapter-navigation
Chapter 2: The RL Problem Formulationhttps://github.com/srikanthbaride/Reinforcement-Learning-Explained-Code/blob/main/ch2_rl_formulation
Chapter 3: Multi-Armed Banditshttps://github.com/srikanthbaride/Reinforcement-Learning-Explained-Code/blob/main/ch3_multi_armed_bandits
Chapter 4: Dynamic Programming Approacheshttps://github.com/srikanthbaride/Reinforcement-Learning-Explained-Code/blob/main/ch4_dynamic_programming
Chapter 5: Monte Carlo Methodshttps://github.com/srikanthbaride/Reinforcement-Learning-Explained-Code/blob/main/ch5_monte_carlo
Chapter 6: Temporal-Difference Learninghttps://github.com/srikanthbaride/Reinforcement-Learning-Explained-Code/blob/main/ch6_td_learning
Chapter 7: TD Control — SARSA and Q-Learninghttps://github.com/srikanthbaride/Reinforcement-Learning-Explained-Code/blob/main/ch7_td_control
Chapter 8: Eligibility Traces and TD(λ)https://github.com/srikanthbaride/Reinforcement-Learning-Explained-Code/blob/main/ch8_td_lambda
Chapter 9: Model-Based RL and Planninghttps://github.com/srikanthbaride/Reinforcement-Learning-Explained-Code/blob/main/ch9_model_based_planning
Chapter 10: Function Approximation Basicshttps://github.com/srikanthbaride/Reinforcement-Learning-Explained-Code/blob/main/ch10_function_approx
Chapter 11: Policy Gradient Fundamentals (REINFORCE)https://github.com/srikanthbaride/Reinforcement-Learning-Explained-Code/blob/main/ch11_policy_gradient
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