Learning to Run NIPS 2017 Competition
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Updated
Aug 18, 2017 - Python
Learning to Run NIPS 2017 Competition
PyTorch implementations of various Deep Reinforcement Learning (DRL) algorithms for both single agent and multi-agent.
Implementing reinforcement-learning algorithms for pysc2 -environment
Attempt to implement A2C and PPO algorithm with modular properties of Maxout and LWTA. # UNFINISHED AND FAILED
Implementation of proximal policy optimization(PPO) with tensorflow
Proximal Policy Optimization with Stein Control Variates:
Multiple Reinforcement learning techniques on 3x3 TicTacToe
PyTorch implementation of Advantage Actor Critic (A2C), Proximal Policy Optimization (PPO) and Scalable trust-region method for deep reinforcement learning using Kronecker-factored approximation (ACKTR). Python2 compatible (branch python2)
Simple reinforcement learning framework for selfplay experiments
RLbox: Solving OpenAI Gym with TensorFlow
MLP-framework (pure numpy) and DDQN-framework for OpenAI's Gym games. +test code for PPO added. +Hindsight Experience Replay(HER) bitflip-DQN example. +prioritized replay.
simple and compact implementations of reinforcement learning benchmark algorithms
Reinforcement learning library for PyTorch.
OpenAI Baselines: high-quality implementations of reinforcement learning algorithms
Projects or classes about ML
This is an pytorch implementation of Distributed Proximal Policy Optimization(DPPO).
小时候练手的rl项目
Minimal implementation of PPO, running in Mujoco env, using Gym-mujoco
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