Offline reinforcement learning (RL), which refers to decision-making from a previously-
collected dataset of interactions, has received significant attention over the past years. Much …

Learning is crucial for automated negotiation, and recent years have witnessed a
remarkable achievement in application of reinforcement learning (RL) for various …

Offline inverse reinforcement learning (Offline IRL) aims to recover the structure of rewards
and environment dynamics that underlie observed actions in a fixed, finite set of …

Offline inverse reinforcement learning (Offline IRL) aims to recover the structure of rewards
and environment dynamics that underlie observed actions in a fixed, finite set of …

Offline meta-reinforcement learning (OMRL) aims to train agents to quickly adapt to new
tasks using only pre-collected data. However, existing OMRL methods often involve …

Offline reinforcement learning (RL) provides a promising solution to learning an agent fully
relying on a data-driven paradigm. However, constrained by the limited quality of the offline …

We present a representation-driven framework for reinforcement learning. By representing
policies as estimates of their expected values, we leverage techniques from contextual …

A prominent challenge of offline reinforcement learning (RL) is the issue of hidden
confounding: unobserved variables may influence both the actions taken by the agent and …

The performance of offline reinforcement learning (RL) suffers from the limited size and
quality of static datasets. Model-based offline RL addresses this issue by generating …

We propose a novel approach for training large language models (LLMs) to adhere to
objectives defined within a latent embedding space. Our method leverages reinforcement …