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PyTorch implementation of Advantage Actor Critic (A2C), Proximal Policy Optimization (PPO), Scalable trust-region method for deep reinforcement learning using Kronecker-factored approximation (ACKTR) and Generative Adversarial Imitation Learning (GAIL).
PyTorch implementations of various Deep Reinforcement Learning (DRL) algorithms for both single agent and multi-agent.
ROS 2 enabled Machine Learning algorithms
Distributed Online Service Coordination Using Deep Reinforcement Learning
Minimal Implementation of Deep RL Algorithms in PyTorch
Deep reinforcement learning framework for fast prototyping based on PyTorch
Interfacing RL agents with user-definable neural networks and OpenAI-gym environments.
Exhaustive Implementation of Algorithms, Key Papers, and Well-Known Problems of Reinforcement Leaning
Some model free RL algorithms
Implementation of A2C and ACKTR in TensorFlow.
Teaching a neural network how to write letters and digits with reinforcement learning.
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)