Name		Name	Last commit message	Last commit date
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ADVERSARY A3C FOR ROBUST REINFORCEMENT.pdf		ADVERSARY A3C FOR ROBUST REINFORCEMENT.pdf
Addressing Function Approximation Error in Actor-Critic Methods.pdf		Addressing Function Approximation Error in Actor-Critic Methods.pdf
Agent57 Outperforming the Atari Human Benchmark.pdf		Agent57 Outperforming the Atari Human Benchmark.pdf
Asynchronous Methods for Deep Reinforcement Learning.pdf		Asynchronous Methods for Deep Reinforcement Learning.pdf
Discovering Reinforcement Learning Algorithms.pdf		Discovering Reinforcement Learning Algorithms.pdf
Evaluating Agents without Rewards.pdf		Evaluating Agents without Rewards.pdf
Never Give Up Learning Directed Exploration Strategies.pdf		Never Give Up Learning Directed Exploration Strategies.pdf
Proximal Policy Optimization Algorithms.pdf		Proximal Policy Optimization Algorithms.pdf
policy-gradient-methods-for-reinforcement-learning-with-function-approximation.pdf		policy-gradient-methods-for-reinforcement-learning-with-function-approximation.pdf
readme.md		readme.md
sample efficient reinforcement learning with stochastic ensemble value expansion.pdf		sample efficient reinforcement learning with stochastic ensemble value expansion.pdf

readme.md

Classical Reinforcement Learning Algorithms

Asynchronous Methods for Deep Reinforcement Learning

Propose A3C and some asynchronous parallel algorthims for RL.

Proximal Policy Optimization Algorithms

Propose PPO.

Implicit Quantile Networks for Distributional Reinforcement Learning

Propose IQN.

Evaluating Agents without Rewards

Proposed by Hafner who proposes Dreamer, PlaNet. The paper seems to be interesting.

Discovering Reinforcement Learning Algorithms

Agent57 Outperforming the Atari Human Benchmark

Propose Agent57 and outperforms than human baseline in all Atari games.