PARAMETRIZED DEEP Q-NETWORKS LEARNING: PLAYING ONLINE BATTLE ARENA WITH DISCRETE-CONTINUOUS HYBRID ACTION SPACE

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Summary

This paper proposes a parametrized deep Q-network (P-DQN) farmework for the hybrid action space without approximation or relaxation and can be viewed as an extension of the DQN to hybrid actions.

Type
article
Published
2018-02-15
Cited by
2
References
35

Keywords

Computer science, Reinforcement learning, Action (physics), Set (abstract data type), Space (punctuation)

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