New algorithm optimizes action masking in reinforcement learning

Researchers have developed a new algorithm for reinforcement learning that addresses the computational bottleneck of determining permissible actions. This algorithm, based on deterministic optimistic optimization, is shown to be minimax optimal and computationally efficient, though sub-optimal on non-degenerate instances. An alternative approach is proposed that plans its exploration strategy, offering a formal interpretation as an analogue of information-directed-sampling for deterministic feedback, and is applied to transfer learning scenarios where invalid actions change over time. AI

IMPACT This research could improve the efficiency of reinforcement learning agents by optimizing the process of determining valid actions, potentially speeding up training and enabling new applications in transfer learning.

RANK_REASON Academic paper detailing a new algorithm for reinforcement learning. [lever_c_demoted from research: ic=1 ai=1.0]

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• arXiv
• deterministic optimistic optimization
• Markov decision process
• reinforcement learning
• transfer learning

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