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Variable-Decision Frequency Option Critic

2022-12-06 19:51:12
Amirmohammad Karimi, Jun Jin, Jun Luo, A. Rupam Mahmood, Martin Jagersand, Samuele Tosatto

Abstract

In classic reinforcement learning algorithms, agents make decisions at discrete and fixed time intervals. The physical duration between one decision and the next becomes a critical hyperparameter. When this duration is too short, the agent needs to make many decisions to achieve its goal, aggravating the problem's difficulty. But when this duration is too long, the agent becomes incapable of controlling the system. Physical systems, however, do not need a constant control frequency. For learning agents, it is desirable to operate with low frequency when possible and high frequency when necessary. We propose a framework called Continuous-Time Continuous-Options (CTCO), where the agent chooses options as sub-policies of variable durations. Such options are time-continuous and can interact with the system at any desired frequency providing a smooth change of actions. The empirical analysis shows that our algorithm is competitive w.r.t. other time-abstraction techniques, such as classic option learning and action repetition, and practically overcomes the difficult choice of the decision frequency.

Abstract (translated)

URL

https://arxiv.org/abs/2212.04407

PDF

https://arxiv.org/pdf/2212.04407.pdf


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