Try to implement strong RL-agent
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Updated
Mar 6, 2017 - Python
Try to implement strong RL-agent
submission to TTI-Chicago programming requirement
Algorithm for learning how to perform tasks with only pixels and rewards as the agents understanding of the environment. The agent can learn how to play various atari games.
A Tensorflow based implementation of "Asynchronous Methods for Deep Reinforcement Learning": https://arxiv.org/abs/1602.01783
PyTorch implementation of Advantage async actor-critic Algorithms (A3C) in PyTorch
MXNET + OpenAI Gym implementation of A3C from "Asynchronous Methods for Deep Reinforcement Learning"
My experimentations with Reinforcement Learning in Pytorch
Keras implementation of Curiosity-driven Exploration by Self-supervised Prediction
Tensorflow implementation of A3C algorithm
A3C Algorithm for classic Atari games
Berkeley CS 294: Deep Reinforcement Learning
Implementing Asynchronous Advantage Actor Critic (A3C) from "Asynchronous Methods for Deep Reinforcement Learning". using TensorFlow
Use Asynchronous advantage actor-critic algorithm (A3C) to play Flappy Bird using Keras
Tensorflow implementation of the asynchronous advantage actor-critic (a3c) reinforcement learning algorithm for continuous action space
Deep Reinforcement Learning with pytorch & visdom (the branch for A3C continuous control)
Implement A3C for Mujoco gym envs
This is a simple implementation of DeepMind's PySC2 RL agents.
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