Algorithm for learning how to perform tasks with only pixels and rewards as the agents understanding of the environment. The agent can learn how to play various atari games.
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Updated
Mar 15, 2017 - Python
Algorithm for learning how to perform tasks with only pixels and rewards as the agents understanding of the environment. The agent can learn how to play various atari games.
小时候练手的rl项目
My implementations of popular reinforcement learning methods based on other developers and research papers.
Attempt to implement A2C and PPO algorithm with modular properties of Maxout and LWTA. # UNFINISHED AND FAILED
Asynchronous Advantage Actor-Critic using Generalized Advantage Estimation (PyTorch)
This project allows to train reinforcement learning agents in pysc2 environments.
A simple A3C algorithm for google research football
Classic reinforce learning algorithms' implementation. It will be merged with study-path repo. Implemented by Pytorch.
Open-zero is a research project aiming to realize the various projects of the company DeepMind
PyTorch-based reinforcement learning implementation of A3C and DQN algorithms for a 2-player Catan environment
PyTorch implementation of A3C (Asynchronous Advantage Actor Critic)
I utilized the A3C (Asynchronous Advantage Actor-Critic) algorithm to train a Deep Q-Learning (DQN) model, specifically tailored to solve the Kungfu gym environment.
Asynchronous Advantage Actor-Critic (A3C) algorithm for Crossy Road
The Asynchronous Advantage Actor Critic (A3C) algorithm is one of the newest algorithms to be developed under the field of Deep Reinforcement Learning Algorithms. This algorithm was developed by Google’s DeepMind which is the Artificial Intelligence division of Google. In this repository, I have my implementations of A3C on Cartpole game, Robot …
PyTorch implementation of Asynchronous (and Synchronous) Advantage Actor Critic
Implementation of some deep RL algorithms
Implementation of RL Algorithms with PyTorch.
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