├── .DS_Store ├── .gitignore ├── 01 Model-Free RL ├── .DS_Store ├── 001 Playing Atari with Deep Reinforcement Learning.md ├── 002 Deep Recurrent Q-Learning for Partially Observable MDPs.md ├── 003 Dueling Network Architectures for Deep Reinforcement Learning.md ├── 004 Deep Reinforcement Learning with Double Q-learning.md ├── 005 Prioritized Experience Replay.md ├── 006 Rainbow Combining Improvements in Deep Reinforcement Learning.md ├── 007 Asynchronous Methods for Deep Reinforcement Learning.md ├── 008 Trust Region Policy Optimization.md ├── 009 High-Dimensional Continuous Control Using Generalized Advantage Estimation.md ├── 010 Proximal Policy Optimization Algorithms.md ├── 011 Emergence of Locomotion Behaviors in Rich Environments.md ├── 012 Scalable trust-region method for deep reinforcement learning using Kronecker-factored approximation.md ├── 013 Sample Efficient Actor-Critic with Experience Replay.md ├── 014 Soft Actor-Critic Off Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor.md ├── 015 Deterministic Policy Gradient Algorithms.md ├── 016 Continuous control with deep reinforcement learning.md ├── 017 Addressing Function Approximation Error in Actor-Critic Methods.md ├── 018 A Distributional Perspective on Reinforcement Learning.md ├── 022 Q-Prop Sample-Efficient Policy Gradient with An Off-Policy Critic.md ├── 023 Action-dependent Control Variates for Policy Optimization via Stein's Identity.md └── 081 Concrete Problems in AI Safety.md ├── 02 Exploration └── 039 Exploration by Random Network Distillation.md ├── 03 Transfer and Multitask RL ├── 043 Progressive Neural Networks.md ├── 044 Universal Value Function Approximators.md ├── 045 Reinforcement Learning with Unsupervised Auxiliary Tasks.md └── 050 Hindsight Experience Replay.md ├── 06 Model-Based RL ├── 059 Imagination-Augmented Agents for Deep Reinforcement Learning.md ├── 060 Neural Network Dynamics for Model-Based Deep Reinforcement Learning with Model-Free Fine-Tuning.md ├── 061 Model-Based Value Expansion for Efficient Model-Free Renforcement Learning.md ├── 066 Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm.md └── 067 Thinking Fast and Slow with Deep Learning and Tree Search.md ├── 07 Meta-RL ├── .DS_Store ├── 068 RL2 Fast Reinforcement Learning via Slow Reinforcement Learning.md ├── 069 Learning to Reinforcement Learn.md └── 070 Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks.md ├── README.md └── imgs ├── .DS_Store ├── 001_1.png ├── 001_2.jpg ├── 002_1.png ├── 002_2.png ├── 003_1.png ├── 003_2.png ├── 003_3.png ├── 003_4.png ├── 003_5.png ├── 003_6.png ├── 004_1.png ├── 005_1.png ├── 005_2.png ├── 005_3.png ├── 006_1.png ├── 007_1.png ├── 008_0.png ├── 008_1.png ├── 008_2.png ├── 008_3.png ├── 009_1.png ├── 009_2.png ├── 009_3.png ├── 009_4.png ├── 009_5.png ├── 009_6.png ├── 010_0.png ├── 010_1.png ├── 010_2.png ├── 010_3.png ├── 010_4.png ├── 010_5.png ├── 010_6.png ├── 011_1.png ├── 011_2.png ├── 011_3.png ├── 012_1.png ├── 013_1.png ├── 013_10.png ├── 013_2.png ├── 013_3.png ├── 013_4.png ├── 013_5.png ├── 013_6.png ├── 013_7.png ├── 013_8.png ├── 013_9.png ├── 014_1.png ├── 014_2.png ├── 014_3.png ├── 014_4.png ├── 015_1.png ├── 015_10.png ├── 015_11.png ├── 015_2.png ├── 015_3.png ├── 015_4.png ├── 015_5.png ├── 015_6.png ├── 015_7.png ├── 015_8.png ├── 015_9.png ├── 016_1.png ├── 016_2.png ├── 017_1.png ├── 017_2.png ├── 017_3.png ├── 017_4.png ├── 022_1.png ├── 022_2.png ├── 022_3.png ├── 023_1.png ├── 023_2.png ├── 023_3.png ├── 023_4.png ├── 039_1.png ├── 039_2.png ├── 043_1.png ├── 043_2.png ├── 044_1.png ├── 044_2.png ├── 050_1.png ├── 059_1.png ├── 059_2.png ├── 060_1.png ├── 060_2.png ├── 060_3.png ├── 060_4.png ├── 060_5.png ├── 060_6.png ├── 061_1.png ├── 067_1.png ├── 068_1.png ├── 070_1.png ├── 070_2.png ├── 070_3.png └── 070_4.png /.DS_Store: -------------------------------------------------------------------------------- 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