Á¤º¸°úÇÐȸ³í¹®Áö (Journal of KIISE)
Current Result Document :
ÇѱÛÁ¦¸ñ(Korean Title) |
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¿µ¹®Á¦¸ñ(English Title) |
Model-Based Reinforcement Learning with Discriminative Loss |
ÀúÀÚ(Author) |
Áø ±¤
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Guang Jin
Yohwan Noh
DoHoon Lee
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¿ø¹®¼ö·Ïó(Citation) |
VOL 47 NO. 06 PP. 0547 ~ 0552 (2020. 06) |
Çѱ۳»¿ë (Korean Abstract) |
°ÈÇнÀÀº ¿©·¯ °¡Áö ¾î·Á¿î ¹®Á¦µéÀ» ÇØ°áÇÏ´Â µ¥ ÁÁÀº °á°ú¸¦ º¸¿©ÁÖ°í ÀÖ´Ù. ±×·¯³ª À̸¦ ½ÇÁ¦ ¹®Á¦¿¡ Àû¿ëÇϱ⿡´Â »ùÇà ȿÀ²¼ºÀÌ Å« ¹®Á¦ÀÌ´Ù. ÀÌ ³í¹®¿¡¼´Â Â÷º° ¼Õ½ÇÇÔ¼ö¸¦ ÀÌ¿ëÇÑ ¸ðµ¨±â¹Ý °ÈÇнÀ ÇÁ·¹ÀÓ¿öÅ©¸¦ Á¦¾ÈÇÑ´Ù. ÀÌ ¹æ¹ýÀº ¸ðµ¨ÀÌ ¼·Î ´Ù¸¥ µ¿ÀÛÀ» ±¸º°ÇÒ ¼ö ÀÖµµ·Ï ÈÆ·ÃÇÑ´Ù. ÀÌ ÇÁ·¹ÀÓ¿öÅ©·Î »çÀü ÇнÀµÈ ÀÎÄÚ´õ°¡ ÃßÃâÇÑ Æ¯Â¡Àº Á¤Ã¥ ±×¶óµð¾ðÆ® ¹æ¹ýÀÌ ÃßÃâÇÑ Æ¯Â¡°ú ÀÏÄ¡ÇÑ´Ù´Â °ÍÀ» ¹ß°ßÇß´Ù. Á¦¾ÈÇÑ ¹æ¹ýÀº ¾ÆŸ¸®(Atari) °ÔÀÓ È¯°æ¿¡¼ ±âÁ¸ÀÇ ¸ðµ¨±â¹Ý °ÈÇнÀ ¹æ¹ýº¸´Ù ³ôÀº »ùÇà ȿÀ²¼ºÀ» º¸¿´À¸¸ç ƯÈ÷ ÇнÀÀÇ Ãʱ⠴ܰ迡¼´Â ±âÁؼ±º¸´Ùµµ ³ôÀº È¿À²¼ºÀ» º¸¿´´Ù
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¿µ¹®³»¿ë (English Abstract) |
Reinforcement learning is a framework for training the agent to make a good sequence of decisions through interacting with a complex environment. Although reinforcement learning has shown promising results in many tasks, sample efficiency still remains a major challenge for its real world application. We propose a novel model-based reinforcement learning framework that incorporates the discriminative loss function, in which models are trained to discriminate one action from another. The encoder pre-trained in this framework shows the feature alignment property, which aligns with the policy gradient method. The proposed method showed better sample efficiency than conventional model-based reinforcement learning approaches in the Atari game environment. In the early stage of the training, the proposed method surpassed the baseline by a large margin
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Å°¿öµå(Keyword) |
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reinforcement learning
model-based RL
discriminative loss function
action discriminatio
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