PyTorch implementations of various Deep Reinforcement Learning (DRL) algorithms for both single agent and multi-agent.
-
Updated
Nov 11, 2017 - Python
PyTorch implementations of various Deep Reinforcement Learning (DRL) algorithms for both single agent and multi-agent.
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)
Implementation of A2C and ACKTR in TensorFlow.
Interfacing RL agents with user-definable neural networks and OpenAI-gym environments.
ROS 2 enabled Machine Learning algorithms
ROS 2 enabled Machine Learning algorithms
Teaching a neural network how to write letters and digits with reinforcement learning.
Minimal Implementation of Deep RL Algorithms in PyTorch
☔ Deep RL agents with PyTorch☔
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).
Deep reinforcement learning framework for fast prototyping based on PyTorch
Distributed Online Service Coordination Using Deep Reinforcement Learning
Add a description, image, and links to the acktr topic page so that developers can more easily learn about it.
To associate your repository with the acktr topic, visit your repo's landing page and select "manage topics."