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
Implementation of Scheduled Policy Optimization for task-oriented language grouding
This is an pytorch implementation of Distributed Proximal Policy Optimization(DPPO).
Proximal Policy Optimization implementation with Tensorflow
Proximal Policy Optimization in PyTorch
Ball Balancing on a Plate in Unity Environment, using Unity ML Agent Plugin
Policy Optimization with Penalized Point Probability Distance: an Alternative to Proximal Policy Optimization
TensorFlow implementation of Proximal Policy Optimization for use in Atari environments
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
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