771 search results - page 8 / 155 » Markov Decision Processes with Arbitrary Reward Processes |
ICML
2007
IEEE
16 years 15 days ago
2007
IEEE
This paper investigates the problem of automatically learning how to restructure the reward function of a Markov decision process so as to speed up reinforcement learning. We begi...
ICML
2001
IEEE
16 years 15 days ago
2001
IEEE
Hierarchical reinforcement learning (RL) is a general framework which studies how to exploit the structure of actions and tasks to accelerate policy learning in large domains. Pri...
115
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AAAI
2007
15 years 2 months ago
2007
In timed, zero-sum games, the goal is to maximize the probability of winning, which is not necessarily the same as maximizing our expected reward. We consider cumulative intermedi...
104
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AIPS
2006
15 years 1 months ago
2006
Researchers often express probabilistic planning problems as Markov decision process models and then maximize the expected total reward. However, it is often rational to maximize ...
104
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ICML
2004
IEEE
16 years 15 days ago
2004
IEEE
We consider learning in a Markov decision process where we are not explicitly given a reward function, but where instead we can observe an expert demonstrating the task that we wa...