Learning to Take Concurrent Actions

We investigate a general semi-Markov Decision Process (SMDP) framework for modeling concurrent decision making, where agents learn optimal plans over concurrent temporally extended actions. We introduce three types of parallel termination schemes { all, any and continue { and theoretically and experimentally compare them.

Authors

• Khashayar Rohanimanesh
• Sridhar Mahadevan Adobe Research

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