Browsing by Author "Chu, Yeong-Jia Roger"

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An Agent for EinStein Wurfelt Nicht! Using N-Tuple Networks
(2017-01-01)
Chu, Yeong-Jia Roger
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Chen, Yuan-Hao
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Hsueh, Chu-Hsuan
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Wu, I-Chen
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資訊工程學系
This paper describes the implementation of an agent that plays EinStein wurfelt nicht!. The agent is based on the common Monte-Carlo tree search (MCTS) which is especially good at dealing with the randomness in a game. For the agent, this paper proposes to use n-tuple networks trained by Monte-Carlo learning. In the agent, the trained n-tuple network is used together with MCTS by the following three approaches: progressive bias, prior knowledge and c-greedy. The experimental results show that epsilon-greedy improved the playing strength the most, which obtained a win rate of 61.05% against the baseline agent. By combining all three approaches, the win rate increased a little to 62.25%. And the enhanced agent also won the first place in the EinStein wurfelt nicht! tournament in Computer Olympiad 2017.
Combining Deep Deterministic Policy Gradient with Cross-Entropy Method
(2019-01-01)
Lai, Tung-Yi
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Hsueh, Chu-Hsuan
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Lin, You-Hsuan
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Chu, Yeong-Jia Roger
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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.