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| """ | |
| --- | |
| title: Reinforcement Learning Algorithms | |
| summary: > | |
| This is a collection of PyTorch implementations/tutorials of reinforcement learning algorithms. | |
| It currently includes Proximal Policy Optimization, Generalized Advantage Estimation, and | |
| Deep Q Networks. | |
| --- | |
| # Reinforcement Learning Algorithms | |
| * [Proximal Policy Optimization](ppo) | |
| * [This is an experiment](ppo/experiment.html) that runs a PPO agent on Atari Breakout. | |
| * [Generalized advantage estimation](ppo/gae.html) | |
| * [Deep Q Networks](dqn) | |
| * [This is an experiment](dqn/experiment.html) that runs a DQN agent on Atari Breakout. | |
| * [Model](dqn/model.html) with dueling network | |
| * [Prioritized Experience Replay Buffer](dqn/replay_buffer.html) | |
| [This is the implementation for OpenAI game wrapper](game.html) using `multiprocessing`. | |
| """ |