Adaptive Mixing of Policies from Searching and Policies from Learning
Researchers propose an architecture called Flexer, which combines policies from neural networks with those generated by Monte Carlo tree search. This approach aims to reduce the time spent on searching for optimal policies in reinforcement learning. Flexer mixes the two types of policies at each step, favoring the MCTS policy as the quality of the network and environment models' variance increases. The authors tested Flexer on three toy symbolic problems and found it outperfo
Researchers propose an architecture called Flexer, which combines policies from neural networks with those generated by Monte Carlo tree search. This approach aims to reduce the time spent on searching for optimal policies in reinforcement learning. Flexer mixes the two types of policies at each step, favoring the MCTS policy as the quality of the network and environment models' variance increases. The authors tested Flexer on three toy symbolic problems and found it outperforms AlphaZero, DQN, and ADP in some cases.
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Why it matters: This matters because reinforcement learning often requires extensive searching for optimal policies, which can be time-consuming. Flexer's approach could potentially speed up this process by leveraging the strengths of both neural networks and MCTS.
Source: https://arxiv.org/abs/2608.15700
This article was originally published at: https://arxiv.org/abs/2608.15700