Reinforcement-Learning-Code
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20 hours ago •
dongminlee94
dongminlee94
A repository for code of reinforcement learning algorithms with PyTorch
Reinforcement Learning Code with PyTorch
Papers
- Deep Q-Network (DQN)
- Double DQN (DDQN)
- Advantage Actor-Critic (A2C)
- Asynchronous Advantage Actor-Critic (A3C)
- Deep Deterministic Policy Gradient (DDPG)
- Truncated Natural Policy Gradient (TNPG)
- Trust Region Policy Optimization (TRPO)
- Generalized Advantage Estimator (GAE)
- Proximal Policy Optimization (PPO)
- Soft Actor-Critic (SAC)
- Apprenticeship Learning via Inverse Reinforcement Learning (APP)
- Maximum Entropy Inverse Reinforcement Learning (MaxEnt)
- Generative Adversarial Imitation Learning (GAIL)
- Variational Adversarial Imitation Learning (VAIL)
Algorithms
01. Model-Free Reinforcement Learning
Deep Q-Network (DQN)
Double DQN (DDQN)
Advantage Actor-Critic (A2C)
Asynchronous Advantage Actor-Critic (A3C)
- CartPole(Classic control)
Deep Deterministic Policy Gradient (DDPG)
Truncated Natural Policy Gradient (TNPG)
Trust Region Policy Optimization (TRPO)
TRPO + Generalized Advantage Estimator (GAE)
Proximal Policy Optimization (PPO)
PPO + Generalized Advantage Estimator (GAE)
Soft Actor-Critic (SAC)
- Pendulum(Classic control)
- Hopper(MoJoCo)
02. Inverse Reinforcement Learning
Apprenticeship Learning via Inverse Reinforcement Learning (APP)
Maximum Entropy Inverse Reinforcement Learning (MaxEnt)
Generative Adversarial Imitation Learning (GAIL)
Variational Adversarial Imitation Learning (VAIL)
Learning curve
CartPole
Pendulum
Hopper
Reference
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dongminlee94
Metadata
A repository for code of reinforcement learning algorithms with PyTorch