Reinforcement Learning
Diversify & Conquer: Outcome-directed Curriculum RL via Out-of-Distribution Disagreement
D2C requires only a few examples of desired outcomes and works in any environment, regardless of its geometry or the distribution of the desired outcome examples.
Sample-Efficient and Safe Deep Reinforcement Learning via Reset Deep Ensemble Agents
We show that the proposed method significantly improves the performance of the baseline algorithm in the considered environments.
Reducing Blackwell and Average Optimality to Discounted MDPs via the Blackwell Discount Factor
We introduce the Blackwell discount factor for Markov Decision Processes (MDPs). Classical objectives for MDPs include discounted, average, and Blackwell opti-mality.
Offline Multi-Agent Reinforcement Learning with Implicit Global-to-Local Value Regularization Xiangsen Wang
Despite some success in the single-agent setting, offline multi-agent RL (MARL) remains to be a challenge. The large joint state-action space and the coupled multi-agent behaviors pose extra complexities for offline policy optimization.