Difference between revisions of "Allen's REINFORCE notes"
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=== Motivation === | === Motivation === | ||
| − | Recall that the objective of Reinforcement Learning is to find an optimal policy <math>\pi^*<\math> which we encode in a neural network with parameters <math>\theta^*< | + | Recall that the objective of Reinforcement Learning is to find an optimal policy <math>\pi^*<\math> which we encode in a neural network with parameters <math>\theta^*</math>. These optimal parameters are defined as |
| − | <math>\theta^* = \text<argmax>_\theta E_{\tau \sim p_\theta(\tau)} \left[ \sum_t r(s_t, a_t) \right] < | + | <math>\theta^* = \text<argmax>_\theta E_{\tau \sim p_\theta(\tau)} \left[ \sum_t r(s_t, a_t) \right] </math> |
=== Learning === | === Learning === | ||
Revision as of 21:42, 24 May 2024
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
