Reinforcement Learning
One Demonstration is Enough to Learn Robot Policies
Sequential decision-making problems typically require significant human supervision and data.
Efficient Policy Adaptation with Contrastive Prompt Ensemble for Embodied Agents
Honguk Woo is the corresponding author.
Supplementary Material to Bi-Level Offline Policy Optimization with Limited Exploration
The "replay" subset consists of samples
Bi-Level Offline Policy Optimization with Limited Exploration
Subsequently, at the upper level, the policy aims to maximize a conservative value estimate from the confidence set formed at the lower level.
Model-Free Active Exploration in Reinforcement Learning
We study the problem of exploration in Reinforcement Learning and present a novel model-free solution. We adopt an information-theoretical viewpoint and start from the instance-specific lower bound of the number of samples that have to be collected to identify a nearly-optimal policy.
Accelerating Value Iteration with Anchoring
In this paper, we present the first accelerated VI for both the Bellman consistency and optimality operators.