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Allen's REINFORCE notes

Revision as of 21:42, 24 May 2024 by Allen12 (talk | contribs) (Motivation)

Allen's REINFORCE notes

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Motivation

Recall that the objective of Reinforcement Learning is to find an optimal policy Failed to parse (unknown function "\math"): {\displaystyle \pi^*<\math> which we encode in a neural network with parameters <math>\theta^*} . These optimal parameters are defined as Failed to parse (syntax error): {\displaystyle \theta^* = \text<argmax>_\theta E_{\tau \sim p_\theta(\tau)} \left[ \sum_t r(s_t, a_t) \right] }

Learning

Learning involves the agent taking actions and the environment returning a new state and reward.

  • Input:  : States at each time step
  • Output:  : Actions at each time step
  • Data:  
  • Learn   to maximize  

State vs. Observation