Allen's REINFORCE notes
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Motivation
Recall that the objective of Reinforcement Learning is to find an optimal policy which we encode in a neural network with parameters . 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
