PyTorch implementations of various Deep Reinforcement Learning (DRL) algorithms for both single agent and multi-agent.
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Updated
Nov 11, 2017 - Python
PyTorch implementations of various Deep Reinforcement Learning (DRL) algorithms for both single agent and multi-agent.
Course projects of CS395T Numerical Optimization, UT Austin
Implementing reinforcement-learning algorithms for pysc2 -environment
PyTorch implementation of Proximal Policy Optimization
RLbox: Solving OpenAI Gym with TensorFlow
OpenAI Baselines: high-quality implementations of reinforcement learning algorithms
This is an pytorch implementation of Distributed Proximal Policy Optimization(DPPO).
Proximal Policy Optimization implementation with Tensorflow
Proximal Policy Optimization in PyTorch
Policy Optimization with Penalized Point Probability Distance: an Alternative to Proximal Policy Optimization
Proximal Policy Optimization
Implementation of Generatve Adversarial Imitation Learning (GAIL) for classic environments from OpenAI Gym.
Reinforcement learning with musculoskeletal models
Nabi Deep Reinforcement Learning with PPO
Proximal Policy Optimization(PPO) with Intrinsic Curiosity Module(ICM)
A repo with a MultiProcessing class for Gym Reinforcement Learning Environments
Exploration of deep reinforcement learning and various state-of-the-art techniques to create a turely autonomous agent.
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