Reinforcement Learning
Corruption-Robust Offline Reinforcement Learning with General Function Approximation
These authors contributed equally to this work.
DualDICE: Behavior-Agnostic Estimation of Discounted Stationary Distribution Corrections
Ofir Nachum, Yinlam Chow, Bo Dai, Lihong Li
Neural Information Processing Systems http://nips.cc/
Learning to Influence Human Behavior with Offline Reinforcement Learning
When interacting with people, AI agents do not just influence the state of the world - they also influence the actions people take in response to the agent, and even their underlying intentions and strategies.
Improving Exploration in Evolution Strategies for Deep Reinforcement Learning via a Population of Novelty-Seeking Agents
Edoardo Conti, Vashisht Madhavan, Felipe Petroski Such, Joel Lehman, Kenneth Stanley, Jeff Clune
Neural Information Processing Systems http://nips.cc/
Divergence-Augmented Policy Optimization
Qing Wang, Yingru Li, Jiechao Xiong, Tong Zhang
In deep reinforcement learning, policy optimization methods need to deal with issues such asfunction approximation andthereuse ofoff-policydata.
Diffusion Policies Creating a Trust Region for Offline Reinforcement Learning
Offline reinforcement learning (RL) leverages pre-collected datasets to train optimal policies. Diffusion Q-Learning (DQL), introducing diffusion models as a powerful and expressive policy class, significantly boosts the performance of offline RL. However, its reliance on iterative denoising sampling to generate actions slows down both training and inference.
Goal-conditioned Imitation Learning
Yiming Ding, Carlos Florensa, Pieter Abbeel, Mariano Phielipp
Neural Information Processing Systems http://nips.cc/