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AAAI
1998
15 years 4 months ago
Tree Based Discretization for Continuous State Space Reinforcement Learning
Reinforcement learning is an effective technique for learning action policies in discrete stochastic environments, but its efficiency can decay exponentially with the size of the ...
William T. B. Uther, Manuela M. Veloso
NIPS
1993
15 years 4 months ago
Robust Reinforcement Learning in Motion Planning
While exploring to nd better solutions, an agent performing online reinforcement learning (RL) can perform worse than is acceptable. In some cases, exploration might have unsafe, ...
Satinder P. Singh, Andrew G. Barto, Roderic A. Gru...
RAS
2000
161views more  RAS 2000»
15 years 3 months ago
Active object recognition by view integration and reinforcement learning
A mobile agent with the task to classify its sensor pattern has to cope with ambiguous information. Active recognition of three-dimensional objects involves the observer in a sear...
Lucas Paletta, Axel Pinz
161
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AI
1999
Springer
15 years 2 months ago
Cooperative Behavior Acquisition for Mobile Robots in Dynamically Changing Real Worlds Via Vision-Based Reinforcement Learning a
In this paper, we first discuss the meaning of physical embodiment and the complexity of the environment in the context of multi-agent learning. We then propose a vision-based rei...
Minoru Asada, Eiji Uchibe, Koh Hosoda
ICML
2005
IEEE
16 years 4 months ago
Dynamic preferences in multi-criteria reinforcement learning
The current framework of reinforcement learning is based on maximizing the expected returns based on scalar rewards. But in many real world situations, tradeoffs must be made amon...
Sriraam Natarajan, Prasad Tadepalli