Reinforcement-Learning
PyTorch implementations of algorithms from "Reinforcement Learning: An Introduction by Sutton and Barto", along with various RL research papers.
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Download Latest Version (.zip)- model_final21551787.pth
- PPO_Continuous_Actions_Ant-v5_final_GAE.ptc
- PPO_Continuous_Actions_HalfCheetah-v5_final_GAE.ptc
- PPO_continuous_BipedalWalker-v3_final.ptc
- PPO_continuous_Pendulum-v1_final.ptc
- SAC_HalfCheetah-v5_final.pt
- actor_critic.png
- actor_critic_cartpole.gif
- actor_critic_cartpole_rewards.png
- blackjack_actions.png
- blackjack_qvals.png
- carpole_info.png
- cliff_walking_double_qlearning.gif
- cliff_walking_expected_sarsa.gif
- cliff_walking_gamma0.75_alpha0.1_epsilon0.1.png
- cliff_walking_gamma0.75_alpha0.1_epsilon0.png
- cliff_walking_gamma0.8_alpha0.1_epsilon0.1.png
- cliff_walking_gamma0.95_alpha0.1_epsilon0.1.png
- cliff_walking_gamma0.99_alpha0.01_epsilon0.png
- cliff_walking_gamma0.99_alpha0.1_epsilon0.1.png
- cliff_walking_gamma0.99_alpha0.1_epsilon0.png
- cliff_walking_gamma0.9_alpha0.1_epsilon0.1.png
- cliff_walking_qlearning.gif
- cliff_walking_sarsa.gif
- connect2_full_game_tree.png
- ddpg_on_Pendulum-v1.png
- ddpg_pendulum.gif
- dqn_pong.gif
- dyna_q_num_planning_steps.png
- dyna_q_num_planning_steps_zoomed.png
- dyna_q_vs_dyna_qplus.png
- expected_sarsa.png
- expert_policy_imitation_learning_animation.gif
- frozen_lake_policy_iteration.gif
- frozen_lake_policy_iteration.mp4
- frozen_slippery_lake_policy_iteration.gif
- frozen_slippery_lake_policy_iteration.mp4
- grpo_algo.png
- imitation_learning_loss.png
- learner_policy_imitation_learning_animation.gif
- Loss.svg
- loss_dqn.png
- lunar_lander.gif
- LunarLander-v3.gif
- LunarLander-v3_combined_plots.png
- mc_blackjack_value_iteration1.gif
- mcts_algorithm_flowchart.png
- mcts_figure.png
- mcts_steps.png
- monte_carlo_policy_gradient.png
- monte_carlo_policy_gradient_cartpole_train_graph.png
- pong_action.png
- pong_game_img.png
- PPO_Cont_Actions_Ant-v5_GAE_training_curves.png
- PPO_Cont_Actions_BipedalWalker-v3_training_curves.png
- PPO_Cont_Actions_HalfCheetah-v5_GAE_training_curves.png
- PPO_Continuous_Actions_Ant-v5_GAE.gif
- PPO_Continuous_Actions_BipedalWalker-v3.gif
- PPO_Continuous_Actions_BipedalWalker-v3_deterministic.gif
- PPO_Continuous_Actions_BipedalWalker-v3_less_training.gif
- PPO_Continuous_Actions_BipedalWalker-v3_less_training.png
- PPO_Continuous_Actions_HalfCheetah-v5_GAE.gif
- PPO_HalfCheetah-v5_loss_charts_tensorboard.png
- PPO_pendulumv1.gif
- PPO_pendulumv1_sum_rewards.png
- PPO_tensorboard_log_HalfCheetah-v5_sum_rewards.png
- prioritized_sweeping_maze_env_num_training_curves.png
- prioritized_sweeping_maze_env_sum_rewards.png
- rollout_sumrewads_imitation_learning.png
- sac_adaptive_alpha_inverteddoublependulum-v5_.gif
- SAC_HalfCheetah-v5_.gif
- sac_inverteddoublependulum-v5_.gif
- sac_rewards_adaptive_alpha_HalfCheetah-v5.png
- sac_rewards_adaptive_alpha_InvertedDoublePendulum-v5.png
- sac_rewards_InvertedDoublePendulum-v5.png
- sac_timesteps_adaptive_alpha_InvertedDoublePendulum-v5.png
- semi_grad_sarsa.png
- shortcut_maze_after_Dyna-Q+_with_25_planning_steps.gif
- shortcut_maze_after_Dyna-Q_with_25_planning_steps.gif
- shortcut_maze_before_Dyna-Q_with_25_planning_steps.gif
- shortcut_maze_prioritized_sweeping_maze_env.gif
- steps_per_ep_dqn.png
- Steps_per_Episode.svg
- Sum_of_Reward.svg
- sum_reward_tensorboard.png
- sum_rewards.png
- Taxi-v3_value_iteration.gif
- Taxi-v3_value_iteration1.gif
- Taxi-v3_value_iteration2.gif
- lr_schedulers.py
- .gitignore
- actor_critic_cartpole.py
- Conservative_Q_Learning_OfflineRL.md
- Curiosity-drivenExploration.md
- custom_envs.py
- deep_deterministic_policy_gradients_ddpg.py
- dqn.py
- dqn_play.py
- dqnlogs.txt
- dynamic_programming_policy_iteration_frozen_lake.py
- dynamic_programming_value_iteration_taxi_v3_.py
- dynaQ_dynaQplus_shortcut_maze_env.py
- GeneralizedAdvantageEstimation_EligibiltyTrace.md
- imitation_learning.md
- imitation_learning.py
- LICENSE
- mcts_alphaGo.md
- monte_carlo_blackjack.py
- monte_carlo_policy_gradient.py
- naive_mcts_monte_carlo_tree_search_connect2.py
- notes.md
- notes2.md
- ppo_continuous_action_space_BipedalWalker-v3.py
- ppo_continuous_action_space_GAE_Ant-v5.py
- ppo_continuous_action_space_GAE_HalfCheetah-v5.py
- ppo_lunar_lander.py
- prioritized_sweep_Dyna-Q_maze_env.py
- qlearning_sarsa_expectedsarsa_on_cliff_walking.py
- README.md
- requirements.txt
- RLHF.md
- shortcutmaze.py
- soft_actor_critic_inverted_double_pendulum.py
- soft_actor_critic_inverted_HalfCheetah-v5.py
# Installation Guide
1. Get the code
git clone https://github.com/VachanVY/Reinforcement-Learning
Downloads the entire project code from GitHub to your computer.
cd Reinforcement-Learning
Moves into the project folder you just downloaded.
2. Python
Easy RecommendedPrerequisites
pip install -r requirements.txt # INSTALL REQUIRED LIBRARIES
Installs the Python libraries listed in requirements.txt (or similar).
If it runs without errors and prints output in the terminal, it worked.
Pulled directly from this repo's README.
// repository documentation
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