Prioritized Experience Replay

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Experience replay lets online reinforcement learning agents remember and reuse experiences from the past. In prior work, experience transitions were uniformly sampled from a replay memory. However, this approach simply replays transitions at the same frequency that they were originally experienced, regardless of their significance. In this paper we develop a framework for prioritizing experience, so as to replay important transitions more frequently, and therefore learn more efficiently. We use prioritized experience replay in Deep Q-Networks (DQN), a reinforcement learning algorithm that achieved human-level performance across many Atari games. DQN with prioritized experience replay achieves a new state-of-the-art, outperforming DQN with uniform replay on 41 out of 49 games.

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Code Links
Github: cilvrRG/RL

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Reading Group on Reinforcement Learning topics

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TensorFlow implementation of Deep RL (Reinforcement Learning) papers based on deep Q-learning (DQN)

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Deep Q-Network implementation for solving Unity's Banana Collection environment

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Rainbow: Combining Improvements in Deep Reinforcement Learning

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Contains Double Deep Q-Network and Actor-Critic. Both utilize either Prioritized Experience Replay or Vanilla Experience Replay

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Github: rmst/ddpg

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Implementation of Deep Deterministic Policy Gradients using TensorFlow and OpenAI Gym

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Multi-agent deep reinforcement learning research project

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Train an agent to navigate a large world and collect yellow bananas, while avoiding blue bananas. The deep reinforcement learning algorithm is based on value-based method.

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PyTorch Implementations of Augmented Random Search

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A collection of Python modules to solve OpenAI Gym environments with Reinforcement Learning.

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A PyTorch Implementation of SAC-Discrete.

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Keras implementation of DQN on ViZDoom environment

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Preparation for Deep Reinforcement Learning using Google Colab

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Reinforcement Learning

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Reinforcement learning course at Data Science Retreat

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Further explorations into Reinforcement learning with Deep Learning Networks

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An experimentation framework for Reinforcement Learning using OpenAI Gym, Tensorflow, and Keras.

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TensorFlow implementation of Deep Reinforcement Learning papers

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Implementations of deep RL papers and random experimentation

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Project 1 for the Udacity Deep Reinforcement Learning Nanodegree

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Yet another prioritized experience replay buffer implementation.

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Implementation of Reinforcement Learning Algorithms. Python, OpenAI Gym, Tensorflow. Exercises and Solutions to accompany Sutton's Book and David Silver's course.

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Github: Kyushik/DRL

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Repository for codes of 'Deep Reinforcement Learning'

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DQN

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Deep Q-Learning Network in pytorch

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Implementation of DQN for Super Mario Bros. 1-1

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Playing ATARI games using a convolutional autoencoder and an evolutionary algorithm. Team name: badskiersevolved (DeepHack.RL)

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Deep Reinforcement Learning (DQN) based Self Driving Car Control with Vehicle Simulator

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Implementation of the Deep Q-learning algorithm to solve MDPs

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implement of prioritized experience replay

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Persistent advantage learning dueling double DQN for the Arcade Learning Environment

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Double Q learning with Priortized Experience Replay

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Exploration of Experience Replay Variants

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Atari - Deep Reinforcement Learning algorithms in TensorFlow

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Prioritized Experience Replay for Reinforcement Learning

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Implementation of a Dueling Double Deep Q-value Network with tensorflow

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Project 3 of the Udacity Deep Reinforcement Learning Nanodegree

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deep reinforcement learning for personal research

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Contains all resources for my high school senior capstone project which is building a Double Deep-Q Network with prioritized experience replay in order to teach a computer to play Tetris.

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An experiment on using deep learning to play games

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Github: VinF/deer

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DEEp Reinforcement learning framework

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CNTK port of DDQN + Prioritized Experience Replay for OpenAI Baselines

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Deep Q-Network implemented using tensorflow

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