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Key Paper Summary in DRL

This repository contains the summaries of around 100 key papers on deep reinforcement learning listed in on OpenAI Spinning Up.

01. Model-Free RL

a. Deep Q-Learning

[001] Playing Atari with Deep Reinforcement Learning, Mnih et al, 2013. Algorithm: DQN. [arxiv] [summary]

[002] Deep Recurrent Q-Learning for Partially Observable MDPs, Hausknecht and Stone, 2015. Algorithm: Deep Recurrent Q-Learning. [arxiv] [summary]

[003] Dueling Network Architectures for Deep Reinforcement Learning, Wang et al, 2015. Algorithm: Dueling DQN. [arxiv] [summary]

[004] Deep Reinforcement Learning with Double Q-learning, Hasselt et al 2015. Algorithm: Double DQN. [arxiv] [summary]

[005] Prioritized Experience Replay, Schaul et al, 2015. Algorithm: Prioritized Experience Replay (PER). [arxiv] [summary]

[006] Rainbow-Combining Improvements in Deep Reinforcement Learning, Hessel et al, 2017. Algorithm: Rainbow DQN. [arxiv] [summary]

13. Classic Papers in RL or Review

[100] Policy Gradient Methods for Reinforcement Learning with Function Approximation, Sutton et al, 2000. Contributions: Established policy gradient theorem and showed convergence of policy gradient algorithm for arbitrary policy classes. [paper] [summary]

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