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
🐲 Stanford CS234 : Reinforcement Learning
A tool for developing reinforcement learning algorithms focused in stock prediction
Master Thesis project that provides a training framework for two player games. TicTacToe and Othello have already been implemented.
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
Autonomous Aerial Vehicle
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.
Game stat acquisition using neural networks
Collection of policy gradient based RL agents
RLForge: Modular, reusable implementations of Deep-RL algorithms
My pysc2 rl agent
🦾 Utilizing a Deep Deterministic Policy Gradient algorithm to train robotic simulations in continuous action space
Deep Reinforcement Learning gym.
Implementation of DQN and DDQN algorithms in Pytorch to play Atari game
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