強化学習問題のための分布推定アルゴリズム
スポンサーリンク
概要
- 論文の詳細を見る
Estimation of Distribution Algorithms (EDAs) are a promising evolutionary computation method. Due to the use of probabilistic models, EDAs can outperform conventional evolutionary computation. In this paper, EDAs are extended to solve reinforcement learning problems which are a framework for autonomous agents. In the reinforcement learning problems, we have to find out better policy of agents such that it yields a large amount of reward for the agents in the future. In general, such policy can be represented by conditional probabilities of agents actions, given the perceptual inputs. In order to estimate such a conditional probability distribution, Conditional Random Fields (CRFs) by Lafferty (2001) are introduced into EDAs. The reason why CRFs are adopted is that CRFs are able to learn conditional probabilistic distributions from a large amount of input-output data, i.e., episodes in the case of reinforcement learning problems. Computer simulations on Probabilistic Transition Problems and Perceptual Aliasing Maze Problems show the effectiveness of EDA-RL.
論文 | ランダム
- 左心房粘液腫の1治験例
- Photoemission, Inverse Photoemission and Optical Studies of Rare-Earth Hexaborides
- 活性型グレリンとアディポサイトカインの臨床的意義
- 大学評価における評価の視点 (講演・報告記録編 第3回大学評価セミナー(平成12年4月実施)) -- (事例報告(1)評価委員の立場から)
- 脳血管障害後遺症に伴う長期的臥床患者における消化器関連機能変化の検討