RL From Scratch
Reinforcement Learning From Scratch
Collection of RL algorithms implemented from scratch in PyTorch. From Q-Learning to Multi-Agent RL, explore fundamental and advanced reinforcement learning concepts.
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A2C (A2C)
A2C (A2C)
Implementation of A2C reinforcement learning algorithm
DDPG
DDPG
Implementation of DDPG reinforcement learning algorithm
DQN Frozenlake
DQN Frozenlake
Implementation of DQN-FrozenLake reinforcement learning algorithm
DQN Lunar
DQN Lunar
Implementation of DQN-Lunar reinforcement learning algorithm
DQN Taxi
DQN Taxi
Implementation of DQN-Taxi reinforcement learning algorithm
DQN Atari
DQN Atari
Implementation of DQN-atari reinforcement learning algorithm
DQN
DQN
Implementation of DQN reinforcement learning algorithm
Duel DQN
Duel DQN
Implementation of Duel-DQN reinforcement learning algorithm
Flappybird PPO
Flappybird PPO
Implementation of FlappyBird-PPO reinforcement learning algorithm
Frozen Lake
Frozen Lake
Implementation of Frozen-Lake reinforcement learning algorithm
Imitation Learning
Imitation Learning
Implementation of Imitation Learning reinforcement learning algorithm
MARL
MARL
Implementation of MARL reinforcement learning algorithm
IPPO
IPPO
Implementation of IPPO reinforcement learning algorithm
MAPPO
MAPPO
Implementation of MAPPO reinforcement learning algorithm
Self Play
Self Play
Implementation of Self Play reinforcement learning algorithm
PPO
PPO
Implementation of PPO reinforcement learning algorithm
Atari
Atari
Implementation of Atari reinforcement learning algorithm
MuJoCo
MuJoCo
PPO on MuJoCo benchmark
REINFORCE
REINFORCE
Implementation of REINFORCE reinforcement learning algorithm
RND
RND
Implementation of RND reinforcement learning algorithm
SAC
SAC
Implementation of SAC reinforcement learning algorithm
TD3
TD3
Implementation of TD3 reinforcement learning algorithm