Overcoming Incomplete Perception with Util Distinction Memory
نویسنده
چکیده
This paper presents a method by which a reinforcement learning agent can solve the incomplete perception problem using memory. The agent uses a hidden Markov model (HMM) to represent its internal state space and creates memory capacity by splitting states of the HMM. The key idea is a test to determine when and how a state should be split: the agent only splits a state when doing so will help the agent predict utility. Thus the agent can create only as much memory as needed to perform the task at hand|not as much as would be required to model all the perceivable world. I call the technique UDM, for Utile Distinction Memory.
منابع مشابه
Overcoming Incomplete Perception with Utile Distinction Memory
This paper presents a method by which a reinforcement learning agent can solve the incomplete perception problem using memory. The agent uses a hidden Markov model (HMM) to represent its internal state space and creates memory capacity by splitting states of the HMM. The key idea is a test to determine when and how a state should be split: the agent only splits a state when doing so will help t...
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