https://github.com/scitator/papers

https://github.com/scitator/papers

arxiv deep-learning deep-reinforcement-learning papers reinforcement-learning reinforcement-learning-algorithms

Last synced: 22 days ago
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• Host: GitHub
• URL: https://github.com/scitator/papers
• Owner: Scitator
• Created: 2018-03-09T07:17:12.000Z (over 6 years ago)
• Default Branch: master
• Last Pushed: 2018-06-18T09:20:47.000Z (over 6 years ago)
• Last Synced: 2024-10-04T12:14:18.642Z (about 1 month ago)
• Topics: arxiv, deep-learning, deep-reinforcement-learning, papers, reinforcement-learning, reinforcement-learning-algorithms
• Size: 14.8 MB
• Stars: 50
• Watchers: 12
• Forks: 2
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

Awesome Lists containing this project

README

        
#### 2018-05

- Progress & Compress: A scalable framework for continual learning [[arxiv](https://arxiv.org/abs/1805.06370)] & [[notes](https://github.com/Scitator/papers/blob/master/papers/1805_progress_compress.md)]

- Playing hard exploration games by watching YouTube [[arxiv](https://arxiv.org/abs/1805.11592)] & [[notes](https://github.com/Scitator/papers/blob/master/papers/1805_youtube.md)]

#### 2018-04

- DORA The Explorer: Directed Outreaching Reinforcement Action-Selection [[arxiv](https://arxiv.org/abs/1804.04012)] & [[notes](https://github.com/Scitator/papers/blob/master/papers/1804_dora.md)]

- Gotta Learn Fast: A New Benchmark for Generalization in RL [[arxiv](https://arxiv.org/abs/1804.03720)] & [[notes](https://github.com/Scitator/papers/blob/master/papers/1804_gotta_learn_fast.md)]

#### 2018-03

- An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling [[arxiv](https://arxiv.org/abs/1803.01271)] & [[notes](https://github.com/Scitator/papers/blob/master/papers/1803_cnn_vs_rnn.md)]

- Generative Multi-Agent Behavioral Cloning [[arxiv](https://arxiv.org/abs/1803.07612)] & [[notes](https://github.com/Scitator/papers/blob/master/papers/1803_behavioral_cloning.md)]

- World Models [[arxiv](https://arxiv.org/abs/1803.10122)] & [[notes](https://github.com/Scitator/papers/blob/master/papers/1803_world_models.md)] 

- Semi-parametric Topological Memory for Navigation [[arxiv](https://arxiv.org/abs/1803.00653)] & [[notes](https://github.com/Scitator/papers/blob/master/papers/1803_sptm.md)] 

- A disciplined approach to neural network hyper-parameters: Part 1 -- learning rate, batch size, momentum, and weight decay [[arxiv](https://arxiv.org/abs/1803.09820)] & [[notes](https://github.com/Scitator/papers/blob/master/papers/1803_smith_part1.md)] 

#### 2018-02

- Model-Ensemble Trust-Region Policy Optimization [[arxiv](https://arxiv.org/abs/1802.10592)] & [[notes](https://github.com/Scitator/papers/blob/master/papers/1802_me_trpo.md)]

#### 2018-01

- Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor [[arxiv](https://arxiv.org/abs/1801.01290)] & [[notes](https://github.com/Scitator/papers/blob/master/papers/1801_soft_ac.md)]

#### 2017-08

- A Brief Survey of Deep Reinforcement Learning [[arxiv](https://arxiv.org/abs/1708.05866)] & [[notes](https://github.com/Scitator/papers/blob/master/papers/1708_rl_survey.md)]

- Reproducibility of Benchmarked Deep Reinforcement Learning Tasks for Continuous Control [[arxiv](https://arxiv.org/abs/1708.04133)] & [[notes](https://github.com/Scitator/papers/blob/master/papers/1708_reproducible_rl.md)]

#### 2017-07

- Distral: Robust Multitask Reinforcement Learning [[arxiv](https://arxiv.org/abs/1707.04175)] & [[notes](https://github.com/Scitator/papers/blob/master/papers/1707_distral.md)]

#### 2017-03

- Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks [[arxiv](https://arxiv.org/abs/1703.03400)] & [[notes](https://github.com/Scitator/papers/blob/master/papers/1703_maml.md)]

#### 2017-02

- Cognitive Mapping and Planning for Visual Navigation [[arxiv](https://arxiv.org/abs/1702.03920)] & [[notes](https://github.com/Scitator/papers/blob/master/papers/1702_cmp.md)]

#### 2016-06

- Progressive Neural Networks [[arxiv](https://arxiv.org/abs/1606.04671)] & [[notes](https://github.com/Scitator/papers/blob/master/papers/1606_progressive_nn.md)]