確率的2分木の行動選択を用いたActor-Criticアルゴリズム

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書誌事項

タイトル別名
  • An Actor-Critic Algorithm Using a Binary Tree Action Selector
  • 確率的2分木の行動選択を用いたActor-Criticアルゴリズム:多数の行動を扱う強化学習
  • カクリツテキ 2ブンギ ノ コウドウ センタク オ モチイタ Actor Critic アルゴリズム タスウ ノ コウドウ オ アツカウ キョウカ ガクシュウ
  • Reinforcement Learning to Cope with Enormous Actions
  • 多数の行動を扱う強化学習

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抄録

In real world applications, learning algorithms often have to handle several dozens of actions, which have some distance metrics. Epsilon-greedy or Boltzmann distribution exploration strategies, which have been applied for Q-learning or SARSA, are very popular, simple and effective in the problems that have a few actions, however, the efficiency would decrease when the number of actions is increased. We propose a policy function representation that consists of a stochastic binary decision tree, and we apply it to an actor-critic algorithm for the problems that have enormous similar actions. Simulation results show the increase of the actions does not affect learning curves of the proposed method at all.

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