Zobrazeno 1 - 5
of 5
pro vyhledávání: '"Rammohan, Sreehari"'
General purpose agents will require large repertoires of skills. Empowerment -- the maximum mutual information between skills and states -- provides a pathway for learning large collections of distinct skills, but mutual information is difficult to o
Externí odkaz:
http://arxiv.org/abs/2307.02728
Mixed Reality (MR) has recently shown great success as an intuitive interface for enabling end-users to teach robots. Related works have used MR interfaces to communicate robot intents and beliefs to a co-located human, as well as developed algorithm
Externí odkaz:
http://arxiv.org/abs/2203.11324
Animals such as rabbits and birds can instantly generate locomotion behavior in reaction to a dynamic, approaching object, such as a person or a rock, despite having possibly never seen the object before and having limited perception of the object's
Externí odkaz:
http://arxiv.org/abs/2203.10616
In this work, we study two self-play training schemes, Chainer and Pool, and show they lead to improved agent performance in Atari Pong compared to a standard DQN agent -- trained against the built-in Atari opponent. To measure agent performance, we
Externí odkaz:
http://arxiv.org/abs/2203.10614
Autor:
Rammohan, Sreehari, Yu, Shangqun, He, Bowen, Hsiung, Eric, Rosen, Eric, Tellex, Stefanie, Konidaris, George
Learning continuous control in high-dimensional sparse reward settings, such as robotic manipulation, is a challenging problem due to the number of samples often required to obtain accurate optimal value and policy estimates. While many deep reinforc
Externí odkaz:
http://arxiv.org/abs/2107.13356