CURL: Contrastive Unsupervised Representation Learning for Sample-Efficient Reinforcement Learning
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Updated
Oct 28, 2020 - Python
CURL: Contrastive Unsupervised Representation Learning for Sample-Efficient Reinforcement Learning
Pytorch LSTM RNN for reinforcement learning to play Atari games from OpenAI Universe. We also use Google Deep Mind's Asynchronous Advantage Actor-Critic (A3C) Algorithm. This is much superior and efficient than DQN and obsoletes it. Can play on many games
Implementation of various Reinforcement Learning Algorithms
A tool for developing reinforcement learning algorithms focused in stock prediction
🐲 Stanford CS234 : Reinforcement Learning
Series of Reinforcement Learning: Q-Learning, Sarsa, SarsaLambda, Deep Q Learning(DQN);一些列强化学习算法,玩OpenAI-gym游戏
🦾 Utilizing a Deep Deterministic Policy Gradient algorithm to train robotic simulations in continuous action space
RLForge: Modular, reusable implementations of Deep-RL algorithms
Master Thesis project that provides a training framework for two player games. TicTacToe and Othello have already been implemented.
My pysc2 rl agent
Deep Reinforcement Learning gym.
Collection of policy gradient based RL agents
Game stat acquisition using neural networks
Autonomous Aerial Vehicle
Implementation of DQN and DDQN algorithms in Pytorch to play Atari game
Reinforcement learning agent which finds a path to the goal in a grid world. This exercise was done as a coursework for course C424 at Imperial College London.
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