Title
Inverse Reinforcement Learning With Evaluation
Abstract
Reinforcement Learning (RL) is a method that helps programming an autonomous agent through humanlike objectives as reinforcements, where the agent is responsible for discovering the best actions to fulfil the objectives. Nevertheless, it is not easy to disentangle human objectives in reinforcement like objectives. Inverse Reinforcement Learning (IRL) determines the reinforcements that a given agent behaviour is fulfilling from the observation of the desired behaviour. In this paper we present a variant of IRL, which is called IRL with Evaluation (IRLE) where instead of observing the desired agent behaviour, the relative evaluation between different behaviours is known by the access to an evaluator. We present also a solution for this problem under the assumption that a relative linear function that preserves the order assumed by the evaluator exists and that the evaluator evaluates policies instead of behaviours. This is posed as a linear feasibility problem, whose solution is well known. Results of simulations of a set of heterogeneous robots in a search and rescue scenario are presented to illustrate the method and the possibility to transfer the learned reinforcement function among robots.
Year
DOI
Venue
2006
10.1109/ROBOT.2006.1642355
2006 IEEE INTERNATIONAL CONFERENCE ON ROBOTICS AND AUTOMATION (ICRA), VOLS 1-10
Keywords
Field
DocType
state transition,learning artificial intelligence,evaluation function,autonomous agents,mobile robots,reinforcement learning,autonomous agent,telerobotics
Autonomous agent,Search and rescue,Artificial intelligence,Engineering,Robot,Linear function,Reinforcement,Telerobotics,Mobile robot,Reinforcement learning
Conference
Volume
Issue
ISSN
2006
1
1050-4729
Citations 
PageRank 
References 
3
0.50
6
Authors
3
Name
Order
Citations
PageRank
Valdinei Freire da Silva1256.86
Anna Helena Reali Costa219231.97
Reali Cost330.50