Deep reinforcement learning agents for OpenAI universe environments
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Updated
Feb 18, 2017 - Python
Deep reinforcement learning agents for OpenAI universe environments
deep reinforcement learning research
Environment that can be used to evaluate reasoning capabilities of artificial agents
PyTorch Implementation of REINFORCE for both discrete & continuous control
Deep-Q-Network reinforcement learning algorithm applied to a simple 2d-car-racing environment
Reinforcement learning solutions
a practice implementation of DQN in open-ai gym
Using pygame to create a 2d pong game, then using gym and tensorflow to read the pixels on the screen using a CNN and then model the actions with a Qlearning RNN to beat the ai opponent
A3C Algorithm for classic Atari games
Run OpenAI Gym on a Server
Tensorflow implementation of the asynchronous advantage actor-critic (a3c) reinforcement learning algorithm for continuous action space
TensorFlow Implementation of Deep Deterministic Policy Gradients for Continuous OpenAI Gym Environments
Distributed RL implementations
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