Project Name | Stars | Downloads | Repos Using This | Packages Using This | Most Recent Commit | Total Releases | Latest Release | Open Issues | License | Language |
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Deep Rl Keras | 516 | 4 years ago | 12 | Python | ||||||
Keras Implementation of popular Deep RL Algorithms (A3C, DDQN, DDPG, Dueling DDQN) | ||||||||||
Async Rl | 44 | 6 years ago | 2 | mit | Python | |||||
Variation of "Asynchronous Methods for Deep Reinforcement Learning" with multiple processes generating experience for agent (Keras + Theano + OpenAI Gym)[1-step Q-learning, n-step Q-learning, A3C] | ||||||||||
A3c_keras_flappybird | 32 | 7 years ago | mit | Python | ||||||
Use Asynchronous advantage actor-critic algorithm (A3C) to play Flappy Bird using Keras | ||||||||||
Rl | 14 | 7 years ago | 6 | Python | ||||||
Reinforcement learning algorithms implemented using Keras and OpenAI Gym | ||||||||||
L2rpn Using A3c | 10 | 5 years ago | 2 | lgpl-3.0 | Python | |||||
Reinforcement Learning using the Actor-Critic framework for the L2RPN challenge (https://l2rpn.chalearn.org/ & https://competitions.codalab.org/competitions/22845#learn_the_details-overview). The agent trained using this code was one of the winners of the challenge. The code runs on the pypownet environment (https://github.com/MarvinLer/pypownet). It is released under a license of LGPLv3 |