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Implementation and Notes of different Reinforcement Learning Algorithms

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Reinforcement-Learning

This repository contains the implementation and notes of different Reinforcement Learning Algorithms and techniques I have learned throughout the Udacity Deep Reinforcement Learning Nanodegree.

Each algorithm is used to solve different OpenAI gym environements.

Algorithms

  1. Monte Carlo
  2. Temporal Difference Algorithms
  3. RL in Continuous Space
  4. Deep Q-Network
  5. Double Deep-Q-Network
  6. Dueling Deep-Q-Network

Projects

  1. Navigation: Train an agent to collect yellow bananas while avoiding blue bananas using Deep Q-learning Algorithm.
  2. Continuous Control: Train an robotic arm to reach target locations using DDPG Algorithm.
  3. Collaboration and Competition: Train a pair of agents to play tennis using MADDPG Algorithm.

Dependency Installation

Follow the instructions given in the Installation_Guide.md to install the dependencies and run the code present in this repository locally.

RL Notes

I have also provided the notes I created while learning the above mentioned algorithms and techniques. You can find these notes in the RL-Notes folder.

  1. The RL Framework
  2. Monte Carlo Methods
  3. Temporal Difference Methods
  4. RL in Continuous Space
  5. Deep Q Networks
    • Deep Q-Network (DQN)
    • Double DQN
    • Prioritized Experience Replay
    • Dueling DQN
    • Rainbow DQN