Dynamic Decision Frequency with Continuous Options
Amirmohammad Karimi,Jun Jin,Jun Luo,A. Rupam Mahmood,Martin Jagersand,Samuele Tosatto,Amirmohammad Karimi,Jun Jin,Jun Luo,A. Rupam Mahmood,Martin Jagersand,Samuele Tosatto
In classic reinforcement learning algorithms, agents make decisions at discrete and fixed time intervals. The duration between decisions becomes a crucial hyperparameter, as setting it too short may increase the problem's difficulty by requiring the agent to make numerous decisions to achieve its goal while setting it too long can result in the agent losing control over the system. However, physic...
