Reinforcement Learning
KALM: Knowledgeable Agent by Offline Reinforcement Learning from Large Language Model Rollouts Jing-Cheng Pang, Si-Hang Y ang, Kaiyuan Li, Xiong-Hui Chen, Nan T ang, Y ang Y u
Reinforcement learning (RL) traditionally trains agents using interaction data, which limits their capabilities to the scope of the training data.
Scaling Laws for Reward Model Overoptimization in Direct Alignment Algorithms Rafael Rafailov Stanford University
A prominent issue with such methods is reward over-optimization or reward hacking, where performance as measured by the learned proxy reward model increases, but true quality plateaus or even deteriorates.
Identifying Latent State-Transition Processes for Individualized Reinforcement Learning Y uewen Sun
The application of reinforcement learning (RL) involving interactions with individuals has grown significantly in recent years.
On the Curses of Future and History in Future-dependent Value Functions for OPE
We study off-policy evaluation (OPE) in partially observable environments with complex observations, with the goal of developing estimators whose guarantee avoids exponential dependence on the horizon.
Distributional Successor Features Enable Zero-Shot Policy Optimization
Intelligent agents must be generalists, capable of quickly adapting to various tasks.
df22a19686a558e74f038e6277a51f68-Paper-Conference.pdf
In tasks aiming for long-term returns, planning becomes essential.