An elegant PyTorch deep reinforcement learning library.
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Updated
Nov 22, 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".
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.
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.
🐋 Simple implementations of various popular Deep Reinforcement Learning algorithms using TensorFlow2
Master classic RL, deep RL, distributional RL, inverse RL, and more using OpenAI Gym and TensorFlow with extensive Math
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)
PyTorch implementation of Soft Actor-Critic (SAC)
Reinforcement Learning Algorithms Based on PyTorch
CURL: Contrastive Unsupervised Representation Learning for Sample-Efficient Reinforcement Learning
Implementation of reinforcement learning approach to make a car learn to drive smoothly in minutes
A PyTorch library for building deep reinforcement learning agents.
RAD: Reinforcement Learning with Augmented Data
JAX (Flax) implementation of algorithms for Deep Reinforcement Learning with continuous action spaces.
Reinforcement learning algorithms implemented for Tensorflow 2.0+ [DQN, DDPG, AE-DDPG, SAC, PPO, Primal-Dual DDPG]
A library for ready-made reinforcement learning agents and reusable components for neat prototyping
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