Allen's REINFORCE notes

Allen's REINFORCE notes

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Learning

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

• Input: $s_{t}$: States at each time step
• Output: $a_{t}$: Actions at each time step
• Data: $(s_{1},a_{1},r_{1},...,s_{T},a_{T},r_{T})$
• Learn $\pi _{\theta }:s_{t}->a_{t}$ to maximize $\sum _{t}r_{t}$