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Simple and easily configurable grid world environments for reinforcement learning
Hello, I pushed some python environments for Multi Agent Reinforcement Learning.
An API standard for multi-agent reinforcement learning environments, with popular reference environments and related utilities
A debugging and profiling tool that can trace and visualize python code execution
Code for "Actor-Attention-Critic for Multi-Agent Reinforcement Learning" ICML 2019
A Comprehensive Reinforcement Learning Zoo for Simple Usage 🚀
Python Multi-Agent Reinforcement Learning framework
Implementations of IQL, QMIX, VDN, COMA, QTRAN, MAVEN, CommNet, DyMA-CL, and G2ANet on SMAC, the decentralised micromanagement scenario of StarCraft II
StarCraft II - pysc2 Deep Reinforcement Learning Examples
PyTorch implementation of Advantage Actor Critic (A2C), Proximal Policy Optimization (PPO), Scalable trust-region method for deep reinforcement learning using Kronecker-factored approximation (ACKT…
PyTorch implementation of DQN, AC, ACER, A2C, A3C, PG, DDPG, TRPO, PPO, SAC, TD3 and ....
Pytorch implementation of Distributed Proximal Policy Optimization: https://arxiv.org/abs/1707.02286
This is an pytorch implementation of Distributed Proximal Policy Optimization(DPPO).
Implementation of Deep Q-learning from Demonstrations using Keras and a Retro Gym environment.
An implement of DQfD（Deep Q-learning from Demonstrations) raised by DeepMind:Learning from Demonstrations for Real World Reinforcement Learning
random search, hill climbing, policy gradient
Implement PPO-clip and PPO-penalty on Atari, which is the only open source of PPO-penalty
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【北京大学】人工智能实践：Tensorflow笔记 手敲代码共享
OpenAI Baselines: high-quality implementations of reinforcement learning algorithms
📡 Simple and ready-to-use tutorials for TensorFlow