11 papers
Overcoming Valid Action Suppression in Unmasked Policy Gradient Algorithms
Renos Zabounidis, Roy Siegelmann, Mohamad Qadri +3
In reinforcement learning environments with state-dependent action validity, action masking consistently outperforms penalty-based handling of invalid actions, yet existing theory…
SCALAR: Learning and Composing Skills through LLM Guided Symbolic Planning and Deep RL Grounding
Renos Zabounidis, Yue Wu, Simon Stepputtis +4
LM-based agents excel when given high-level action APIs but struggle to ground language into low-level control. Prior work has LLMs generate skills or reward functions for RL, but…
Theory of Mind Guided Strategy Adaptation for Zero-Shot Coordination
Andrew Ni, Simon Stepputtis, Stefanos Nikolaidis +3
A central challenge in multi-agent reinforcement learning is enabling agents to adapt to previously unseen teammates in a zero-shot fashion. Prior work in zero-shot coordination of…
B3C: A Minimalist Approach to Offline Multi-Agent Reinforcement Learning
Woojun Kim, Katia Sycara
Overestimation arising from selecting unseen actions during policy evaluation is a major challenge in offline reinforcement learning (RL). A minimalist approach in the single-agent…
Adaptively Coordinating with Novel Partners via Learned Latent Strategies
Benjamin Li, Shuyang Shi, Lucia Romero +7
Adaptation is the cornerstone of effective collaboration among heterogeneous team members. In human-agent teams, artificial agents need to adapt to their human partners in real tim…
Fair Cooperation in Mixed-Motive Games via Conflict-Aware Gradient Adjustment
Woojun Kim, Katia Sycara
Multi-agent reinforcement learning in mixed-motive settings presents a fundamental challenge: agents must balance individual interests with collective goals, which are neither full…