An intelligent agent that adaptively changes its thought processes to maximize cumulative reward
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Updated
Feb 19, 2017
An intelligent agent that adaptively changes its thought processes to maximize cumulative reward
Reinforcement learning algorithms implemented using Keras and OpenAI Gym
Open source implementation of the PAAC algorithm presented in Efficient Parallel Methods for Deep Reinforcement Learning
realisation of reinforcement learning algorithms based with vizdoom
This was more of an experiment to learn how to cuda and parallelize things. Thus, there are many many things that can be improved on. Feel free to use it and contribute if you find it useful.
Exploring Reinforcement Learning using Python and CS:GO
A game bot using OpenAI gym and Reinforcement Learinng
Basic deep reinforcement learning algorithms implemented with Keras
A Python Implementation of Bayesian Inverse Reinforcement Learning (BIRL)
My content of CS294 Deep Reinforcement Learning course, conduced by Sergey Levine from UC Berkeley.
I applied The Thompson Sampling model in both python and R
We use policy gradient to help agents learn optimal policies in a competitive multi-agent contextual bandit setting
I implemented the reinforcement learning based model Upper Confidence Bound in both Python and R
A course on Deep Reinforcement Learning in Computer Vision. Visit Website:
Notes on DL/RL papers I read
This is a test project for to try Reinforcement Learning (Q-Learning) and machine learning on PHP
Open AI Gym version of Berkeley AI Pacman with images as states
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