Memory Allocation in Resource-Constrained Reinforcement Learning

Massimiliano Tamborski; David Abel

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arXiv · 2506.17263

Memory Allocation in Resource-Constrained Reinforcement Learning

Massimiliano Tamborski·David Abel

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Abstract

Resource constraints can fundamentally change both learning and decision-making. We explore how memory constraints influence an agent's performance when navigating unknown environments using standard reinforcement learning algorithms. Specifically, memory-constrained agents face a dilemma: how much of their limited memory should be allocated to each of the agent's internal processes, such as estimating a world model, as opposed to forming a plan using that model? We study this dilemma in MCTS- and DQN-based algorithms and examine how different allocations of memory impact performance in episodic and continual learning settings.

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Massimiliano Tamborski, David Abel. 2025-06-09. Memory Allocation in Resource-Constrained Reinforcement Learning. https://arxiv.org/abs/2506.17263

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