Reinforcement Learning
Exploration Bonus for Regret Minimization in Discrete and Continuous Average Reward MDPs
Jian QIAN, Ronan Fruit, Matteo Pirotta, Alessandro Lazaric
The exploration bonus is an effective approach to manage the exploration-exploitation trade-off in Markov Decision Processes (MDPs).
Convergent Policy Optimization for Safe Reinforcement Learning
Ming Yu, Zhuoran Yang, Mladen Kolar, Zhaoran Wang
Neural Information Processing Systems http://nips.cc/
Unsupervised Curricula for Visual Meta-Reinforcement Learning
Allan Jabri, Kyle Hsu, Abhishek Gupta, Ben Eysenbach, Sergey Levine, Chelsea Finn
Neural Information Processing Systems http://nips.cc/
Guided Meta-Policy Search
Russell Mendonca, Abhishek Gupta, Rosen Kralev, Pieter Abbeel, Sergey Levine, Chelsea Finn
Neural Information Processing Systems http://nips.cc/
Divergence-Augmented Policy Optimization
Qing Wang, Yingru Li, Jiechao Xiong, Tong Zhang
This paper introduces a method to stabilize policy optimization when off-policy data are reused.
Goal-conditioned Imitation Learning
Yiming Ding, Carlos Florensa, Pieter Abbeel, Mariano Phielipp
Furthermore, we are often interested in being able to reach a wide range of configurations, hence setting up a different reward every time might be unpractical. Methods like Hindsight Experience Replay (HER) have recently shown promise to learn policies able to reach many goals, without the need of a reward.
Planning with Goal-Conditioned Policies
Soroush Nasiriany, Vitchyr Pong, Steven Lin, Sergey Levine
Neural Information Processing Systems http://nips.cc/
