Reinforcement Learning
Managing Temporal Resolution in Continuous Value Estimation: A Fundamental Trade-off
We address this gap by analyzing Monte-Carlo policy evaluation for LQR systems and uncover a fundamental trade-off between approximation and statistical error in value estimation.
StateMask: Explaining Deep Reinforcement Learning through State Mask
Most existing methods explain why an agent takes individual actions rather than pinpointing the critical steps to its final reward.
Cal-QL: Calibrated Offline RL Pre-Training for Efficient Online Fine-Tuning
However, existing offline RL methods tend to behave poorly during fine-tuning. In this paper, we study the fine-tuning problem in the context of conservative offline RL methods and we devise an approach for learning an effective initialization from offline data that also enables fast online fine-tuning capabilities.
Survival Instinct in Offline Reinforcement Learning
This phenomenon cannot be easily explained by offline RL's return maximization objective.
Offline Reinforcement Learning with Differential Privacy Anonymous Author(s) Affiliation Address email
Differential privacy (DP) [Dwork et al., 2006] is a well-established definition of privacy with many
