An elegant PyTorch deep reinforcement learning library.
-
Updated
Jun 20, 2024 - Python
An elegant PyTorch deep reinforcement learning library.
PyTorch implementation of DQN, AC, ACER, A2C, A3C, PG, DDPG, TRPO, PPO, SAC, TD3 and ....
PyTorch implementation of Advantage Actor Critic (A2C), Proximal Policy Optimization (PPO), Scalable trust-region method for deep reinforcement learning using Kronecker-factored approximation (ACKTR) and Generative Adversarial Imitation Learning (GAIL).
Massively Parallel Deep Reinforcement Learning. 🔥
Modularized Implementation of Deep RL Algorithms in PyTorch
High-quality single file implementation of Deep Reinforcement Learning algorithms with research-friendly features (PPO, DQN, C51, DDPG, TD3, SAC, PPG)
Implementations of basic RL algorithms with minimal lines of codes! (pytorch based)
Modular Deep Reinforcement Learning framework in PyTorch. Companion library of the book "Foundations of Deep Reinforcement Learning".
PyTorch implementation of Deep Reinforcement Learning: Policy Gradient methods (TRPO, PPO, A2C) and Generative Adversarial Imitation Learning (GAIL). Fast Fisher vector product TRPO.
RL starter files in order to immediately train, visualize and evaluate an agent without writing any line of code
Reinforcement learning tutorials
🐋 Simple implementations of various popular Deep Reinforcement Learning algorithms using TensorFlow2
PyTorch implementations of various Deep Reinforcement Learning (DRL) algorithms for both single agent and multi-agent.
This repository contains most of pytorch implementation based classic deep reinforcement learning algorithms, including - DQN, DDQN, Dueling Network, DDPG, SAC, A2C, PPO, TRPO. (More algorithms are still in progress)
XuanCe: A Comprehensive and Unified Deep Reinforcement Learning Library
A PyTorch library for building deep reinforcement learning agents.
Recurrent and multi-process PyTorch implementation of deep reinforcement Actor-Critic algorithms A2C and PPO
A library for ready-made reinforcement learning agents and reusable components for neat prototyping
Add a description, image, and links to the a2c topic page so that developers can more easily learn about it.
To associate your repository with the a2c topic, visit your repo's landing page and select "manage topics."