pytorch-DRL
Active·★ 617·MIT·Updated 2017-11-11
★ LLM Infra★ Dev Tooling
PyTorch implementations of various Deep Reinforcement Learning (DRL) algorithms for both single agent and multi-agent.
Pytorch-madrl provides modular PyTorch implementations for a range of Deep Reinforcement Learning (DRL) algorithms, suitable for both single and multi-agent systems. It features a unified agent interface with components for environment interaction, training, and action selection to promote code reusability across different DRL methods.
#PyTorch#Reinforcement Learning#Deep Learning#Multi-Agent#DRL Algorithms
01
Features
01Modular PyTorch implementation of DRL algorithms
02Supports both single and multi-agent deep reinforcement learning
03Unified agent interface for core functionalities (interact, train, action selection)
04Includes implementations of A2C, ACKTR, DQN, DDPG, PPO
05Components for environment interaction and experience collection
02
Compatibility
PyTorch
Native
Verified via docs
Gym
Supported
Verified via docs
03
Use cases
↳Developing and experimenting with various deep reinforcement learning algorithms
↳Researching and comparing single and multi-agent DRL performance
↳Building AI agents for simulated environments using PyTorch
04
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