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ICML
2006
IEEE

An intrinsic reward mechanism for efficient exploration

9 years 9 months ago
An intrinsic reward mechanism for efficient exploration
How should a reinforcement learning agent act if its sole purpose is to efficiently learn an optimal policy for later use? In other words, how should it explore, to be able to exploit later? We formulate this problem as a Markov Decision Process by explicitly modeling the internal state of the agent and propose a principled heuristic for its solution. We present experimental results in a number of domains, also exploring the algorithm's use for learning a policy for a skill given its reward function--an important but neglected component of skill discovery.
Özgür Simsek, Andrew G. Barto
Added 17 Nov 2009
Updated 17 Nov 2009
Type Conference
Year 2006
Where ICML
Authors Özgür Simsek, Andrew G. Barto
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