Browsing by Author "Lai, Tung-Yi"

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Combining Deep Deterministic Policy Gradient with Cross-Entropy Method
(2019-01-01)
Lai, Tung-Yi
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Hsueh, Chu-Hsuan
;
Lin, You-Hsuan
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Chu, Yeong-Jia Roger
;
Hsueh, Bo-Yang
This paper proposes a deep reinforcement learning algorithm for solving robotic tasks, such as grasping objects. We propose in this paper a combination of cross-entropy optimization (CE) with deep deterministic policy gradient (DDPG). More specifically, where in the CE method, we first sample from a Gaussian distribution with zero as its initial mean, we now set the initial mean to DDPG's output instead. The resulting algorithm is referred to as the DDPG-CE method. Next, to negate the effects of bad samples, we improve on DDPG-CE by substituting the CE component with a weighted CE method, resulting in the DDPG-WCE algorithm. Experiments show that DDPG-WCE achieves a higher success rate on grasping previously unseen objects, than other approaches, such as supervised learning, DDPG, CE, and DDPG-CE.
Multilabeled Value Networks for Computer Go
(2018-12-01)
Wu, Ti-Rong
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Wu, I-Chen
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Chen, Guan-Wun
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Wei, Ting-Han
;
Wu, Hung-Chun
This paper proposes a new approach to a novel value network architecture for the game Go, called a multilabeled (ML) value network. In the ML value network, different values (win rates) are trained simultaneously for different settings of komi, a compensation given to balance the initiative of playing first. The ML value network has three advantages: 1) it outputs values for different komi; (2) it supports dynamic komi; and (3) it lowers the mean squared error (MSE). This paper also proposes a new dynamic komi method to improve game-playing strength. This paper also performs experiments to demonstrate the merits of the architecture. First, the MSE of the ML value network is generally lower than the value network alone. Second, the program based on the ML value network wins by a rate of 67.6% against the program based on the value network alone. Third, the program with the proposed dynamic komi method significantly improves the playing strength over the baseline that does not use dynamic komi, especially for handicap games. To our knowledge, up to date, no handicap games have been played openly by programs using value networks. This paper provides these programs with a useful approach to playing handicap games.