Finding the Time to Think: Learning Planning Budgets in Real-Time RL
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Computer Science > Machine Learning
Title:Finding the Time to Think: Learning Planning Budgets in Real-Time RL
Abstract:Deliberating takes time. In real-time settings, that time is not free. Standard reinforcement learning (RL) sidesteps this as the environment waits indefinitely for the agent's decision. Instead, we study real-time RL environments where the environment progresses while waiting for the agent's action. Building on prior real-time formalizations, we introduce variable-delay real-time RL, where the agent chooses how long to deliberate at each decision point since the environment progresses. For the planning agents we use, the right delay is state-dependent, and naively planning how long to plan can paralyze the agent. We instead approach this setting by training a lightweight gating policy on top of a planner to select state-dependent planning budgets. Across real-time Pac-Man, Tetris, Snake, Speed Hex, and Speed Go, our gating policy outperforms fixed-budget and heuristic baselines, and transfers to a real-time setup where the environment and agent run on two different GPUs.
| Subjects: | Machine Learning (cs.LG) |
| Cite as: | arXiv:2606.26463 [cs.LG] |
| (or arXiv:2606.26463v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2606.26463
arXiv-issued DOI via DataCite (pending registration)
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