An elegant PyTorch deep reinforcement learning library.
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Updated
Aug 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 ....
Massively Parallel Deep Reinforcement Learning. 🔥
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".
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)
Python library for Reinforcement Learning.
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)
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.
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
RAD: Reinforcement Learning with Augmented Data
Reinforcement learning library(framework) designed for PyTorch, implements DQN, DDPG, A2C, PPO, SAC, MADDPG, A3C, APEX, IMPALA ...
lagom: A PyTorch infrastructure for rapid prototyping of reinforcement learning algorithms.
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