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Spring 2024
In reinforcement learning, the notion of state plays a central role. A reinforcement learning agent requires the state to evaluate its current situation, select actions, and construct a model of the environment. In the classic setting, it is assumed that the environment provides the agent with...
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Spring 2019
Juan Fernando Hernandez Garcia
Unifying seemingly disparate algorithmic ideas to produce better performing algorithms has been a longstanding goal in reinforcement learning. As a primary example, the TD(λ) algorithm elegantly unifies temporal difference (TD) methods with Monte Carlo methods through the use of eligibility...