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This research introduces Flexer, an architecture in reinforcement learning that dynamically balances neural network policies with Monte Carlo tree search to optimize training efficiency without fixed-depth constraints. By adaptively mixing these approaches based on policy quality and model variance, the method achieves superior performance over standard AlphaZero variants on symbolic problems while reducing computational overhead. This advancement addresses a key bottleneck in RL by making planning processes more responsive to current learning states rather than relying on static schedules.
Adaptive Mixing of Policies from Searching and Policies from Learning
arXiv:2608.15700v1 Announce Type: new Abstract: Background: Distillation of training targets generated thru search/planning has proven useful in reinforcement learning, but search can take exceedingly long. Objectives: Rather than perform search to the same depth every time (typically at a fixed period of steps), reduce the search depth proportionally to the quality of the policy network priors. Methods: We describe Flexer, an architecture that, for each step, mixes the policy from a neural network and the policy from Monte Carlo tree search. The mixing factor favors the MCTS policy as the policy imitation error of the network and the environment models' variance increases. Results: Flexer outperforms a version of AlphaZero (and DQN and ADP) for some experiments on three toy symbolic problems.
Abstract:Background: Distillation of training targets generated thru search/planning has proven useful in reinforcement learning, but search can take exceedingly long. Objectives: Rather than perform search to the same depth every time (typically at a fixed period of steps), reduce the search depth proportionally to the quality of the policy network priors. Methods: We describe Flexer, an architecture that, for each step, mixes the policy from a neural network and the policy from Monte Carlo tree search. The mixing factor favors the MCTS policy as the policy imitation error of the network and the environment models' variance increases. Results: Flexer outperforms a version of AlphaZero (and DQN and ADP) for some experiments on three toy symbolic problems.
Artificial Intelligence (cs.AI)
arXiv:2608.15700 [cs.AI]
(or arXiv:2608.15700v1 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2608.15700 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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