Reinforcement Learning
Information Design in Multi-Agent Reinforcement Learning
To thrive in those environments, the agent needs to influence other agents so their actions become more helpful and less harmful. Research in computational economics distills two ways to influence others directly: by providing tangible goods ( mechanism design) and by providing information ( information design). This work investigates information design problems for a group of RL agents. The main challenges are two-fold. One is the information provided will immediately affect the transition of the agent trajectories, which introduces additional non-stationarity. The other is the information can be ignored, so the sender must provide information that the receiver is willing to respect.
Information Design in Multi-Agent Reinforcement Learning
To thrive in those environments, the agent needs to influence other agents so their actions become more helpful and less harmful. Research in computational economics distills two ways to influence others directly: by providing tangible goods ( mechanism design) and by providing information ( information design). This work investigates information design problems for a group of RL agents. The main challenges are two-fold. One is the information provided will immediately affect the transition of the agent trajectories, which introduces additional non-stationarity. The other is the information can be ignored, so the sender must provide information that the receiver is willing to respect.
Preference-grounded Token-level Guidance for Language Model Fine-tuning Shentao Yang
Aligning language models (LMs) with preferences is an important problem in natural language generation.
AT oolkit for Reliable Benchmarking and Research in Multi-Objective Reinforcement Learning
There are several reasons why reproducibility is often a challenge in RL.
Online Nonstochastic Model-Free Reinforcement Learning
Drawing inspiration from recent advancements in model-based control, we introduce a novel class of policies centered on disturbance signals.
Thinker: Learning to Plan and Act
Stephen Chung, Ivan Anokhin, David Krueger
Deep Reinforcement Learning
Hybrid Search for Efficient Planning with Completeness Guarantees
Solving complex planning problems has been a long-standing challenge in computer science.
SPRING: Studying the Paper and Reasoning to Play Games Yue Wu
Open-world survival games pose significant challenges for AI algorithms due to their multi-tasking, deep exploration, and goal prioritization requirements. Despite reinforcement learning (RL) being popular for solving games, its high sample complexity limits its effectiveness in complex open-world games like Crafter or Minecraft. We propose a novel approach, SPRING, to read Crafter's original academic paper and use the knowledge learned to reason and play the game through a large language model (LLM).