Implement a deep Q-learning network to play a simple game from OpenAI Gym.
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Updated
Sep 8, 2017 - Python
Implement a deep Q-learning network to play a simple game from OpenAI Gym.
Deep-Q-Network by tensorflow
Platform allowing easy analysis of DeepMind's algorithm DQN learning to play Mario Bros. Application provides neural network design tool and statistical tools for analysis.
Clear explanations and simple implementations of Deep reinforcement learning Algorithms
Observe your machine learning to play a video game
Tic-Tac-Toe and other games with Deep Q-Networks
Deep Q network for 5x5 Gomoku with the trained weights.
A deep-reinforcement learning agent that loves bananas.
AI projects and some algorithms. If you are seeing this please take care of the path in the code
Solving the CartPole-v1 problem using Deep Q-Learning
Use Deep Reinforcement Learning to train an agent to navigate in a large, square world and collect bananas.
Value Based and policy gradient Algorithms Implementation on single and multi agent environments.
Q-Learning and Deep Q-Learning agents learning to play Nim
Multi-agent Reinforcement Learing system for efficient item collection and navigation in the raf-rpg-game.
This is one of my deep reinforcement learning experiments done during a university course.
An Implementation of Deep Q Network using TensorFlow
Implemented Deep-Q-Learning on OpenAI's CartPole environment.
Using a Deep Q Network to train an agent to collect only yellow bananas leaving the blue ones in a unity ml-agents environment
This is my attempt at implementing the paper "Playing Atari with Deep Reinforcement Learning" By Volodymyr Mnih, Koray Kavukcuoglu, David Silver, Alex Graves, Ioannis Antonoglou, Daan Wierstra and Martin Riedmiller. This is my first attempt at both reading and implementing a research paper.
We use reinforcement learning for the optimal control and stabilization of pendulum and double pendulum systems, adressing the swing-up problem
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