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pro vyhledávání: '"Laflaquière, Alban"'
Learning optimal policies in sparse rewards settings is difficult as the learning agent has little to no feedback on the quality of its actions. In these situations, a good strategy is to focus on exploration, hopefully leading to the discovery of a
Externí odkaz:
http://arxiv.org/abs/2111.01919
Reward-based optimization algorithms require both exploration, to find rewards, and exploitation, to maximize performance. The need for efficient exploration is even more significant in sparse reward settings, in which performance feedback is given s
Externí odkaz:
http://arxiv.org/abs/2102.03140
Autor:
Laflaquière, Alban
Spatial knowledge is a fundamental building block for the development of advanced perceptive and cognitive abilities. Traditionally, in robotics, the Euclidean (x,y,z) coordinate system and the agent's forward model are defined a priori. We show that
Externí odkaz:
http://arxiv.org/abs/2010.15469
Evolvability is an important feature that impacts the ability of evolutionary processes to find interesting novel solutions and to deal with changing conditions of the problem to solve. The estimation of evolvability is not straightforward and is gen
Externí odkaz:
http://arxiv.org/abs/2005.06224
Performing Reinforcement Learning in sparse rewards settings, with very little prior knowledge, is a challenging problem since there is no signal to properly guide the learning process. In such situations, a good search strategy is fundamental. At th
Externí odkaz:
http://arxiv.org/abs/1909.05508
Despite its omnipresence in robotics application, the nature of spatial knowledge and the mechanisms that underlie its emergence in autonomous agents are still poorly understood. Recent theoretical works suggest that the Euclidean structure of space
Externí odkaz:
http://arxiv.org/abs/1906.01401
Autor:
Laflaquière, Alban, Hafner, Verena V.
This work investigates how a naive agent can acquire its own body image in a self-supervised way, based on the predictability of its sensorimotor experience. Our working hypothesis is that, due to its temporal stability, an agent's body produces more
Externí odkaz:
http://arxiv.org/abs/1906.00825
Akademický článek
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Publikováno v:
Front. Robot. AI, 25 June 2018
Perceiving the surrounding environment in terms of objects is useful for any general purpose intelligent agent. In this paper, we investigate a fundamental mechanism making object perception possible, namely the identification of spatio-temporally in
Externí odkaz:
http://arxiv.org/abs/1810.05057
Sensorimotor contingency theory offers a promising account of the nature of perception, a topic rarely addressed in the robotics community. We propose a developmental framework to address the problem of the autonomous acquisition of sensorimotor cont
Externí odkaz:
http://arxiv.org/abs/1810.01870