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Found 6 papers, 3 papers with code
Learning to Verify Summary Facts with Fine-Grained LLM Feedback
In this paper, we explore alternative way of leveraging Large Language Model (LLM) generated feedback to address the inherent limitation of using human-labeled data.
Diffusion-based Episodes Augmentation for Offline Multi-Agent Reinforcement Learning
Offline multi-agent reinforcement learning (MARL) is increasingly recognized as crucial for effectively deploying RL algorithms in environments where real-time interaction is impractical, risky, or costly.
Preference Alignment with Flow Matching
We present Preference Flow Matching (PFM), a new framework for preference-based reinforcement learning (PbRL) that streamlines the integration of preferences into an arbitrary class of pre-trained models.
Toward Risk-based Optimistic Exploration for Cooperative Multi-Agent Reinforcement Learning
In this paper, we present a risk-based exploration that leads to collaboratively optimistic behavior by shifting the sampling region of distribution.
Distributional Reinforcement Learning
Multi-agent Reinforcement Learning
+4
The StarCraft Multi-Agent Challenges+ : Learning of Multi-Stage Tasks and Environmental Factors without Precise Reward Functions
This challenge, on the other hand, is interested in the exploration capability of MARL algorithms to efficiently learn implicit multi-stage tasks and environmental factors as well as micro-control.
Ranked #1 on
SMAC+
on Off_Superhard_parallel
Risk Perspective Exploration in Distributional Reinforcement Learning
Distributional reinforcement learning demonstrates state-of-the-art performance in continuous and discrete control settings with the features of variance and risk, which can be used to explore.
Distributional Reinforcement Learning
reinforcement-learning
+3
