Reinforcement Learning
A Finite-Sample Analysis of Payoff-Based Independent Learning in Zero-Sum Stochastic Games
A common feature of these applications is that there are multiple decision makers interacting with each other in a common environment.
DIFFER: Decomposing Individual Reward for Fair Experience Replay in Multi-Agent Reinforcement Learning
The code is available in https://github.com/cathyhxh/DIFFER.
SafeDICE: Offline Safe Imitation Learning with Non-Preferred Demonstrations
In this paper, we present a hyperparameter-free offline safe IL algorithm, SafeDICE, that learns safe policy by leveraging the non-preferred demonstrations in the space of stationary distributions. Our algorithm directly estimates the stationary distribution corrections of the policy that imitate the demonstrations excluding the non-preferred behavior.
Reinforcing LLM Agents via Policy Optimization with Action Decomposition
Beginning with the simplification of flattening all actions, we theoretically explore the discrepancies between action-level optimization and this naive token-level optimization.
Integrating Suboptimal Human Knowledge with Hierarchical Reinforcement Learning for Large-Scale Multiagent Systems
While agents' learning is data-driven, sampling from millions
