Reinforcement learning from human feedback (RLHF) can improve the quality of large
language model's (LLM) outputs by aligning them with human preferences. We propose a …

With the continued advancement of Large Language Models (LLMs) Agents in reasoning,
planning, and decision-making, benchmarks have become crucial in evaluating these skills …

Offline reinforcement learning (RL) aims to learn optimal policies from offline datasets,
where the parameterization of policies is crucial but often overlooked. Recently, Diffsuion-QL …

Conservatism has led to significant progress in offline reinforcement learning (RL) where an
agent learns from pre-collected datasets. However, as many real-world scenarios involve …

Effective decision making involves flexibly relating past experiences and relevant contextual
information to a novel situation. In deep reinforcement learning (RL), the dominant paradigm …

Abstract The advancement of Offline Reinforcement Learning (RL) and Offline Multi-Agent
Reinforcement Learning (MARL) critically depends on the availability of high-quality, pre …

Deep neural networks are the most commonly used function approximators in offline
reinforcement learning. Prior works have shown that neural nets trained with TD-learning …

We present a novel perspective on goal-conditioned reinforcement learning by framing it
within the context of denoising diffusion models. Analogous to the diffusion process, where …

StarCraft Multi-Agent Challenge (SMAC) is one of the most commonly used experimental
environments in multi-agent reinforcement learning (MARL), where the specific task is to …

Reinforcement learning encounters formidable challenges when tasked with intricate
decision-making scenarios, primarily due to the expansive parameterized action spaces and …