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This repository uses Reinforcement Learning techniques to build agents capable of training in different OpenAI Gym environments : Classic control, Box2D and Atari

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Reinforcement Learning

Common strategies

The project is explained here and describes how to do Reinforcement Learning with q-learning and neural networks.

  • qlearning_FrozenLake.ipynb notebook explore q-learning technique in FrozenLake openai gym environment:

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  • A neural network was also used to teach an agent to land on a specific position. The following agent was trained for 3k episodes:

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  • The same architecture was used in the CartPole openai gym environment where a pole is attached by an un-actuated joint to a cart, which moves along a frictionless track:

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The architecture and methods of the agent have been designed to adapt to other open gym environments.

Implementation of Deep Mind DQN [IN PROGRESS]

The project is explained here

It explains all the steps involved in building a neural network to play Atari games; how to: get the OpenAi Gym environment, preprocess the environment to feed the CNN, implement the CNN, create the training loop.

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This repository uses Reinforcement Learning techniques to build agents capable of training in different OpenAI Gym environments : Classic control, Box2D and Atari

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