Shypd.aiThe largest open index of AI toolsreinforcement-learning-algorithms is an open-source tool that provides PyTorch implementations of classic deep reinforcement learning algorithms. It offers clear code for learning and experimenting with algorithms like DQN, DDQN, DDPG, SAC, A2C, PPO, and TRPO.
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This repository, reinforcement-learning-algorithms, offers PyTorch implementations of various classic deep reinforcement learning algorithms. It includes popular methods such as Deep Q-Learning Network (DQN), Double Q-Network (DDQN), Dueling Network Architecture, Deep Deterministic Policy Gradient (DDPG), Soft Actor-Critic (SAC), Advantage Actor-Critic (A2C), Proximal Policy Optimization (PPO), and Trust Region Policy Optimization (TRPO). The project aims to provide clear and well-structured code to facilitate learning and experimentation with these algorithms. It also includes utilities for environment wrapping, experience replay, logging, and MPI training, making it a comprehensive resource for developers and researchers in the field.
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