1335 Open source projects that are alternatives of or similar to Dissecting Reinforcement Learning

Reinforcement Learning Tutorial with Demo: DP (Policy and Value Iteration), Monte Carlo, TD Learning (SARSA, QLearning), Function Approximation, Policy Gradient, DQN, Imitation, Meta Learning, Papers, Courses, etc..

Contains high quality implementations of Deep Reinforcement Learning algorithms written in PyTorch

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PyTorch implementation of Soft Actor-Critic (SAC)

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PyTorch implementations of various Deep Reinforcement Learning (DRL) algorithms for both single agent and multi-agent.

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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

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An introductory series to Reinforcement Learning (RL) with comprehensive step-by-step tutorials.

The purpose of this repository is to make prototypes as case study in the context of proof of concept(PoC) and research and development(R&D) that I have written in my website. The main research topics are Auto-Encoders in relation to the representation learning, the statistical machine learning for energy-based models, adversarial generation networks(GANs), Deep Reinforcement Learning such as Deep Q-Networks, semi-supervised learning, and neural network language model for natural language processing.

Deep & Classical Reinforcement Learning + Machine Learning Examples in Python

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A course on Deep Reinforcement Learning in Computer Vision. Visit Website:

Arnold - DOOM Agent

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Package provides java implementation of reinforcement learning algorithms such Q-Learn, R-Learn, SARSA, Actor-Critic

Deep Reinforcement Learning in Autonomous Driving: the A3C algorithm used to make a car learn to drive in TORCS; Python 3.5, Tensorflow, tensorboard, numpy, gym-torcs, ubuntu, latex

Reinforcement Learning Demos

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Python library for Reinforcement Learning.

A life simulation for exploring social dynamics

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