Combining Planning and Reinforcement Learning for Solving Relational Multiagent Domains

Nikhilesh Prabhakar; Ranveer Singh; Harsha Kokel; Sriraam Natarajan; Prasad Tadepalli

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arXiv · 2502.19297

Combining Planning and Reinforcement Learning for Solving Relational Multiagent Domains

Nikhilesh Prabhakar·Ranveer Singh·Harsha Kokel·Sriraam Natarajan·Prasad Tadepalli

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Abstract

Multiagent Reinforcement Learning (MARL) poses significant challenges due to the exponential growth of state and action spaces and the non-stationary nature of multiagent environments. This results in notable sample inefficiency and hinders generalization across diverse tasks. The complexity is further pronounced in relational settings, where domain knowledge is crucial but often underutilized by existing MARL algorithms. To overcome these hurdles, we propose integrating relational planners as centralized controllers with efficient state abstractions and reinforcement learning. This approach proves to be sample-efficient and facilitates effective task transfer and generalization.

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Nikhilesh Prabhakar, Ranveer Singh, Harsha Kokel, Sriraam Natarajan, Prasad Tadepalli. 2025-02-26. Combining Planning and Reinforcement Learning for Solving Relational Multiagent Domains. https://arxiv.org/abs/2502.19297

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