PyTorch implementation of Deep Reinforcement Learning: Policy Gradient methods (TRPO, PPO, A2C) and Generative Adversarial Imitation Learning (GAIL). Fast Fisher vector product TRPO.

An elegant PyTorch deep reinforcement learning library.

Master classic RL, deep RL, distributional RL, inverse RL, and more using OpenAI Gym and TensorFlow with extensive Math

Master Reinforcement and Deep Reinforcement Learning using OpenAI Gym and TensorFlow

DeepRL algorithms implementation easy for understanding and reading with Pytorch and Tensorflow 2(DQN, REINFORCE, VPG, A2C, TRPO, PPO, DDPG, TD3, SAC)

PyTorch implementation of DQN, AC, ACER, A2C, A3C, PG, DDPG, TRPO, PPO, SAC, TD3 and ....

TD-Regularized Actor-Critic Methods

Minimal and Clean Reinforcement Learning Examples

Lightweight deep RL Libraray for continuous control.

Multi-hop knowledge graph reasoning learned via policy gradient with reward shaping and action dropout

A Clearer and Simpler Synchronous Advantage Actor Critic (A2C) Implementation in TensorFlow

Automate swing trading using deep reinforcement learning. The deep deterministic policy gradient-based neural network model trains to choose an action to sell, buy, or hold the stocks to maximize the gain in asset value. The paper also acknowledges the need for a system that predicts the trend in stock value to work along with the reinforcement …

A set of RL experiments. Currently including: (1) the MDP rank experiment, based on policy gradient algorithm

Solutions of assignments of Deep Reinforcement Learning course presented by the University of California, Berkeley (CS285) in Pytorch framework

Explorer is a PyTorch reinforcement learning framework for exploring new ideas.

A course on Deep Reinforcement Learning in Computer Vision. Visit Website:

HandyRL is a handy and simple framework based on Python and PyTorch for distributed reinforcement learning that is applicable to your own environments.

A resource for learning about deep learning techniques from regression to LSTM and Reinforcement Learning using financial data and the fitness functions of algorithmic trading

TensorFlow A2C to solve Acrobot, with synchronized parallel environments

Solving board games like Connect4 using Deep Reinforcement Learning