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| DQN | Playing Atari with Deep Reinforcement Learning (**DQN**) [[Markdown]](https://github.com/datawhalechina/easy-rl/blob/master/papers/DQN/Playing%20Atari%20with%20Deep%20Reinforcement%20Learning.md) [[PDF]](https://github.com/datawhalechina/easy-rl/blob/master/papers/DQN/PDF/Playing%20Atari%20with%20Deep%20Reinforcement%20Learning.pdf) | https://arxiv.org/abs/1312.5602 | |
| | Deep Recurrent Q-Learning for Partially Observable MDPs [[Markdown]](https://github.com/datawhalechina/easy-rl/blob/master/papers/DQN/Deep%20Recurrent%20Q-Learning%20for%20Partially%20Observable%20MDPs.md) [[PDF]](https://github.com/datawhalechina/easy-rl/blob/master/papers/DQN/PDF/Deep%20Recurrent%20Q-Learning%20for%20Partially%20Observable%20MDPs.pdf) | https://arxiv.org/abs/1507.06527 | |
| | Dueling Network Architectures for Deep Reinforcement Learning (**Dueling DQN**) [[Markdown]](https://github.com/datawhalechina/easy-rl/blob/master/papers/DQN/Dueling%20Network%20Architectures%20for%20Deep%20Reinforceme.md) [[PDF]](https://github.com/datawhalechina/easy-rl/blob/master/papers/DQN/PDF/Dueling%20Network%20Architectures%20for%20Deep%20Reinforceme.pdf)| https://arxiv.org/abs/1511.06581 | |
| | Deep Reinforcement Learning with Double Q-learning (**Double DQN**) [[Markdown]](https://github.com/datawhalechina/easy-rl/blob/master/papers/DQN/Deep%20Reinforcement%20Learning%20with%20Double%20Q-learning.md) [[PDF]](https://github.com/datawhalechina/easy-rl/blob/master/papers/DQN/PDF/Deep%20Reinforcement%20Learning%20with%20Double%20Q-learning.pdf)| https://arxiv.org/abs/1509.06461 | |
| | Prioritized Experience Replay (**PER**) [[Markdown]](https://github.com/datawhalechina/easy-rl/blob/master/papers/DQN/Prioritized%20Experience%20Replay.md) [[PDF]](https://github.com/datawhalechina/easy-rl/blob/master/papers/DQN/PDF/Prioritized%20Experience%20Replay.pdf)| https://arxiv.org/abs/1511.05952 | |
| | Rainbow: Combining Improvements in Deep Reinforcement Learning (**Rainbow**) [[Markdown]](https://github.com/datawhalechina/easy-rl/blob/master/papers/DQN/Rainbow_Combining%20Improvements%20in%20Deep%20Reinforcement%20Learning.md) [[PDF]](https://github.com/datawhalechina/easy-rl/blob/master/papers/DQN/PDF/Rainbow_Combining%20Improvements%20in%20Deep%20Reinforcement%20Learning.pdf)| https://arxiv.org/abs/1710.02298 | |
| | Dueling Network Architectures for Deep Reinforcement Learning (**Dueling DQN**) [[Markdown]](https://github.com/datawhalechina/easy-rl/blob/master/papers/DQN/Dueling%20Network%20Architectures%20for%20Deep%20Reinforceme.md) [[PDF]](https://github.com/datawhalechina/easy-rl/blob/master/papers/DQN/PDF/Dueling%20Network%20Architectures%20for%20Deep%20Reinforceme.pdf) | https://arxiv.org/abs/1511.06581 | |
| | Deep Reinforcement Learning with Double Q-learning (**Double DQN**) [[Markdown]](https://github.com/datawhalechina/easy-rl/blob/master/papers/DQN/Deep%20Reinforcement%20Learning%20with%20Double%20Q-learning.md) [[PDF]](https://github.com/datawhalechina/easy-rl/blob/master/papers/DQN/PDF/Deep%20Reinforcement%20Learning%20with%20Double%20Q-learning.pdf) | https://arxiv.org/abs/1509.06461 | |
| | Prioritized Experience Replay (**PER**) [[Markdown]](https://github.com/datawhalechina/easy-rl/blob/master/papers/DQN/Prioritized%20Experience%20Replay.md) [[PDF]](https://github.com/datawhalechina/easy-rl/blob/master/papers/DQN/PDF/Prioritized%20Experience%20Replay.pdf) | https://arxiv.org/abs/1511.05952 | |
| | Rainbow: Combining Improvements in Deep Reinforcement Learning (**Rainbow**) [[Markdown]](https://github.com/datawhalechina/easy-rl/blob/master/papers/DQN/Rainbow_Combining%20Improvements%20in%20Deep%20Reinforcement%20Learning.md) [[PDF]](https://github.com/datawhalechina/easy-rl/blob/master/papers/DQN/PDF/Rainbow_Combining%20Improvements%20in%20Deep%20Reinforcement%20Learning.pdf) | https://arxiv.org/abs/1710.02298 | |
| Policy gradient | Asynchronous Methods for Deep Reinforcement Learning (**A3C**) [[Markdown]](https://github.com/datawhalechina/easy-rl/blob/master/papers/Policy_gradient/Asynchronous%20Methods%20for%20Deep%20Reinforcement%20Learning.md) | https://arxiv.org/abs/1602.01783 | |
| | Trust Region Policy Optimization (**TRPO**) [[Markdown]](https://github.com/datawhalechina/easy-rl/blob/master/papers/Policy_gradient/Trust%20Region%20Policy%20Optimization.md) [[PDF]](https://github.com/datawhalechina/easy-rl/blob/master/papers/Policy_gradient/PDF/Trust%20Region%20Policy%20Optimization.pdf)| https://arxiv.org/abs/1502.05477 | |
| | Trust Region Policy Optimization (**TRPO**) [[Markdown]](https://github.com/datawhalechina/easy-rl/blob/master/papers/Policy_gradient/Trust%20Region%20Policy%20Optimization.md) [[PDF]](https://github.com/datawhalechina/easy-rl/blob/master/papers/Policy_gradient/PDF/Trust%20Region%20Policy%20Optimization.pdf) | https://arxiv.org/abs/1502.05477 | |
| | High-Dimensional Continuous Control Using Generalized Advantage Estimation (**GAE**) [[Markdown]](https://github.com/datawhalechina/easy-rl/blob/master/papers/Policy_gradient/High-Dimensional%20Continuous%20Control%20Using%20Generalized%20Advantage%20Estimation.md) [[PDF]](https://github.com/datawhalechina/easy-rl/blob/master/papers/Policy_gradient/PDF/High-Dimensional%20Continuous%20Control%20Using%20Generalised%20Advantage%20Estimation.pdf) | https://arxiv.org/abs/1506.02438 | |
| | Proximal Policy Optimization Algorithms (**PPO**) [[Markdown]](https://github.com/datawhalechina/easy-rl/blob/master/papers/Policy_gradient/Proximal%20Policy%20Optimization%20Algorithms.md) [[PDF]](https://github.com/datawhalechina/easy-rl/blob/master/papers/Policy_gradient/PDF/Proximal%20Policy%20Optimization%20Algorithms.pdf) | https://arxiv.org/abs/1707.06347 | |
| | Emergence of Locomotion Behaviours in Rich Environments (**PPO-Penalty**) | https://arxiv.org/abs/1707.02286 | |
| | Scalable trust-region method for deep reinforcement learning using Kronecker-factored approximation (**ACKTP**) [[Markdown]](https://github.com/datawhalechina/easy-rl/blob/master/papers/Policy_gradient/Scalable%20trust-region%20method%20for%20deep%20reinforcement%20learning%20using%20Kronecker-factored.md) [[PDF]](https://github.com/datawhalechina/easy-rl/blob/master/papers/Policy_gradient/PDF/Scalable%20trust-region%20method%20for%20deep%20reinforcement%20learning%20using%20Kronecker-factored.pdf)| https://arxiv.org/abs/1708.05144 | |
| | Scalable trust-region method for deep reinforcement learning using Kronecker-factored approximation (**ACKTP**) [[Markdown]](https://github.com/datawhalechina/easy-rl/blob/master/papers/Policy_gradient/Scalable%20trust-region%20method%20for%20deep%20reinforcement%20learning%20using%20Kronecker-factored.md) [[PDF]](https://github.com/datawhalechina/easy-rl/blob/master/papers/Policy_gradient/PDF/Scalable%20trust-region%20method%20for%20deep%20reinforcement%20learning%20using%20Kronecker-factored.pdf) | https://arxiv.org/abs/1708.05144 | |
| | Sample Efficient Actor-Critic with Experience Replay (**ACER**) | https://arxiv.org/abs/1611.01224 | |
| | Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor(**SAC**) [[Markdown]](https://github.com/datawhalechina/easy-rl/blob/master/papers/Policy_gradient/Soft%20Actor-Critic_Off-Policy%20Maximum%20Entropy%20Deep%20Reinforcement%20Learning%20with%20a%20Stochastic%20Actor.md) [[PDF]](https://github.com/datawhalechina/easy-rl/blob/master/papers/Policy_gradient/PDF/Soft%20Actor-Critic_Off-Policy%20Maximum%20Entropy%20Deep%20Reinforcement%20Learning%20with%20a%20Stochastic%20Actor.pdf) | https://arxiv.org/abs/1801.01290 | |
| | Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor (**SAC**) [[Markdown]](https://github.com/datawhalechina/easy-rl/blob/master/papers/Policy_gradient/Soft%20Actor-Critic_Off-Policy%20Maximum%20Entropy%20Deep%20Reinforcement%20Learning%20with%20a%20Stochastic%20Actor.md) [[PDF]](https://github.com/datawhalechina/easy-rl/blob/master/papers/Policy_gradient/PDF/Soft%20Actor-Critic_Off-Policy%20Maximum%20Entropy%20Deep%20Reinforcement%20Learning%20with%20a%20Stochastic%20Actor.pdf) | https://arxiv.org/abs/1801.01290 | |
| | Deterministic Policy Gradient Algorithms (**DPG**) [[Markdown]](https://github.com/datawhalechina/easy-rl/blob/master/papers/Policy_gradient/Deterministic%20Policy%20Gradient%20Algorithms.md) [[PDF]](https://github.com/datawhalechina/easy-rl/blob/master/papers/Policy_gradient/PDF/Deterministic%20Policy%20Gradient%20Algorithms.pdf) | http://proceedings.mlr.press/v32/silver14.pdf | |
| | Continuous Control With Deep Reinforcement Learning (**DDPG**) | https://arxiv.org/abs/1509.02971 | |
| | Addressing Function Approximation Error in Actor-Critic Methods (**TD3**) [[Markdown]](https://github.com/datawhalechina/easy-rl/blob/master/papers/Policy_gradient/Addressing%20Function%20Approximation%20Error%20in%20Actor-Critic%20Methods.md) [[PDF]](https://github.com/datawhalechina/easy-rl/blob/master/papers/Policy_gradient/PDF/Addressing%20Function%20Approximation%20Error%20in%20Actor-Critic%20Methods.pdf)| https://arxiv.org/abs/1802.09477 | |
| | Addressing Function Approximation Error in Actor-Critic Methods (**TD3**) [[Markdown]](https://github.com/datawhalechina/easy-rl/blob/master/papers/Policy_gradient/Addressing%20Function%20Approximation%20Error%20in%20Actor-Critic%20Methods.md) [[PDF]](https://github.com/datawhalechina/easy-rl/blob/master/papers/Policy_gradient/PDF/Addressing%20Function%20Approximation%20Error%20in%20Actor-Critic%20Methods.pdf) | https://arxiv.org/abs/1802.09477 | |
| | A Distributional Perspective on Reinforcement Learning (**C51**) [[Markdown]](https://github.com/datawhalechina/easy-rl/blob/master/papers/Policy_gradient/A%20Distributional%20Perspective%20on%20Reinforcement%20Learning.md) [[PDF]](https://github.com/datawhalechina/easy-rl/blob/master/papers/Policy_gradient/PDF/A%20Distributional%20Perspective%20on%20Reinforcement%20Learning.pdf) | https://arxiv.org/abs/1707.06887 | |
| | | | |
| | Q-Prop: Sample-Efficient Policy Gradient with An Off-Policy Critic (**Q-Prop**) | https://arxiv.org/abs/1611.02247 | |
| | Action-depedent Control Variates for Policy Optimization via Stein’s Identity (**Stein Control Variates**) | https://arxiv.org/abs/1710.11198 | |
| | The Mirage of Action-Dependent Baselines in Reinforcement Learning | https://arxiv.org/abs/1802.10031 | |
| | Bridging the Gap Between Value and Policy Based Reinforcement Learning (**PCL**) | https://arxiv.org/abs/1702.08892 | |