About

This page tracks the roadmap of reinforcement learning study materials, especially for the applications in robot arm.
The study materials include the list of papers, presentation file, youtube links, codes, tutorials, and another paper roadmaps for the study of RL.
Admin: seunghyeok back in GIST AILAB [site]

Papers

Mnih, Volodymyr, et al. "Playing atari with deep reinforcement learning." arXiv preprint arXiv:1312.5602 (2013).
Mnih, Volodymyr, et al. "Human-level control through deep reinforcement learning." Nature 518.7540 (2015): 529.
Van Hasselt, Hado, Arthur Guez, and David Silver. "Deep Reinforcement Learning with Double Q-Learning." AAAI. Vol. 2. (2016). [pdf] [ppt]*
Wang, Ziyu, et al. "Dueling Network Architectures for Deep Reinforcement Learning." ICML. 2016.
Schaul, Tom, et al. "Prioritized experience replay." ICLR. (2016). [pdf]*
S. Gu et al., "Continuous Deep Q-Learning with Model-based Acceleration", ICML, 2016. [pdf]*
"Towards Vision-Based Deep Reinforcement Learning for Robotic Motion Control", F. Zhang et al., arXiv, 2015. [pdf]*
Deep Reinforcement Learning in Parameterized Action Space, M. Hausknecht et al., ICLR, 2016. [pdf]*
TD. Kulkarni et al., "Deep Successor Reinforcement Learning", arXiv, 2016. [pdf]*
Aravind S. L. et al., "Dynamic Frarme skip Deep Q Network", arXiv, 2016.

Mnih, Volodymyr, et al. "Asynchronous methods for deep reinforcement learning" International conference on machine learning. 2016. [pdf] [ppt] by Seunghyeok Back
Levine, Sergey, et al. "End-to-end training of deep visuomotor policies." The Journal of Machine Learning Research 17.1 (2016): 1334-1373.
Schulman, John, et al. "Trust region policy optimization." International Conference on Machine Learning. 2015.
Schulman, John, et al. "Proximal policy optimization algorithms." arXiv preprint arXiv:1707.06347 (2017).

Levine, Sergey, et al. "Learning hand-eye coordination for robotic grasping with deep learning and large-scale data collection." The International Journal of Robotics Research 37.4-5 (2018): 421-436.
Zhu, Yuke, et al. "Reinforcement and imitation learning for diverse visuomotor skills." arXiv preprint arXiv:1802.09564 (2018).
Xie, Annie, et al. "Few-shot goal inference for visuomotor learning and planning." arXiv preprint arXiv:1810.00482 (2018).
Yu, Tianhe, et al. "One-Shot Hierarchical Imitation Learning of Compound Visuomotor Tasks." arXiv preprint arXiv:1810.11043 (2018).

Wulfmeier, Markus, Ingmar Posner, and Pieter Abbeel. "Mutual alignment transfer learning." arXiv preprint arXiv:1707.07907 (2017).
Rusu, Andrei A., et al. "Sim-to-Real Robot Learning from Pixels with Progressive Nets." Conference on Robot Learning. 2017.
Zhang, Fangyi, et al. "Adversarial Discriminative Sim-to-real Transfer of Visuo-motor Policies." arXiv preprint arXiv:1709.05746 (2017).
Zhang, Chao, Yang Yu, and Zhi-Hua Zhou. "Learning Environmental Calibration Actions for Policy Self-Evolution." IJCAI. 2018.
Tan, Jie, et al. "Sim-to-Real: Learning Agile Locomotion For Quadruped Robots." arXiv preprint arXiv:1804.10332 (2018).

Haber, Nick, et al. "Learning to Play with Intrinsically-Motivated Self-Aware Agents." arXiv preprint arXiv:1802.07442 (2018).

Ho, Jonathan, and Stefano Ermon. "Generative adversarial imitation learning." Advances in Neural Information Processing Systems. 2016.

PyRobot: hardware independent APIs for robotic manipulation and naviagion [github]
PyRep: a toolkit for robot learning research, built on top of the V-REP [github]

CS294-112 at UC Berkeley, 2018: Deep Reinfocement Learning [site], [youtube]
COMPM050/COMPGI13 at UCL, 2015: Reinforcement Learning [site], [youtube]
Deep RL Course from beginner to expert with codes [site], [codes]
Robot Ignite Academy: Online ROS Courses [site]
ROS Developers LIVE Class [site]