An elegant PyTorch deep reinforcement learning library.
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Updated
Nov 24, 2024 - Python
An elegant PyTorch deep reinforcement learning library.
PyTorch implementation of DQN, AC, ACER, A2C, A3C, PG, DDPG, TRPO, PPO, SAC, TD3 and ....
Massively Parallel Deep Reinforcement Learning. 🔥
Implementations of basic RL algorithms with minimal lines of codes! (pytorch based)
Clean, Robust, and Unified PyTorch implementation of popular Deep Reinforcement Learning (DRL) algorithms (Q-learning, Duel DDQN, PER, C51, Noisy DQN, PPO, DDPG, TD3, SAC, ASL)
Modular Deep Reinforcement Learning framework in PyTorch. Companion library of the book "Foundations of Deep Reinforcement Learning".
Python library for Reinforcement Learning.
32 projects in the framework of Deep Reinforcement Learning algorithms: Q-learning, DQN, PPO, DDPG, TD3, SAC, A2C and others. Each project is provided with a detailed training log.
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)
A PyTorch library for building deep reinforcement learning agents.
JAX (Flax) implementation of algorithms for Deep Reinforcement Learning with continuous action spaces.
🐋 Simple implementations of various popular Deep Reinforcement Learning algorithms using TensorFlow2
CURL: Contrastive Unsupervised Representation Learning for Sample-Efficient Reinforcement Learning
PyTorch implementation of Soft Actor-Critic (SAC)
Reinforcement Learning Algorithms Based on PyTorch
DrQ: Data regularized Q
Reinforcement learning library(framework) designed for PyTorch, implements DQN, DDPG, A2C, PPO, SAC, MADDPG, A3C, APEX, IMPALA ...
RAD: Reinforcement Learning with Augmented Data
lagom: A PyTorch infrastructure for rapid prototyping of reinforcement learning algorithms.
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