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Reinforcement Learning with Limited Reinforcement: Using Bayes Risk for Active Learning in POMDPs
Finale Doshi1, Joelle Pineau, Nicholas Roy
1Massachusetts Institute of Technology, Boston, USA, FINALE@MIT.EDU.
Proceedings of the ... International Conference on Machine Learning. International Conference on Machine Learning
|May 15, 2010
Summary
This study introduces a novel approach for Partially Observable Markov Decision Processes (POMDPs) by treating model parameters as hidden states. This method enables agents to actively learn optimal policies in complex planning domains.
Area of Science:
- Artificial Intelligence
- Robotics
- Reinforcement Learning
Background:
- Partially Observable Markov Decision Processes (POMDPs) are effective for planning problems balancing knowledge acquisition and reward maximization.
- Specifying POMDP parameters solely from domain knowledge is challenging due to their complexity.
Purpose of the Study:
- To present an approximation approach for POMDPs that addresses parameter specification challenges.
- To enable agents to actively learn optimal policies by incorporating model uncertainty.
Main Methods:
- Developed a "model-uncertainty" POMDP framework where model parameters are treated as additional hidden states.
- Integrated model-directed queries to facilitate active policy learning.
Main Results:
- Demonstrated the effectiveness of the proposed approximation approach on various POMDP problems.
- Showcased the planner's ability to actively learn good policies.
Conclusions:
- The "model-uncertainty" POMDP approach offers a viable solution for handling parameter specification issues.
- This method enhances agent learning capabilities in POMDP domains, leading to improved policy generation.
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