A2C Loss Function Formulation

An agent is being trained using a policy gradient method. The objective is to maximize the function $U = \sum_{t} \log \pi_{\theta}(a_t|s_t)A(s_t, a_t)$, where $\pi_{\theta}$ is the policy and $A$ is the advantage function which indicates how much better an action is than the average.

At a specific state $s$, the agent can choose from three actions: $a_1, a_2, a_3$. The calculated advantage values for these actions are:

• $A(s, a_1) = +2.5$
• $A(s, a_2) = -1.0$
• $A(s, a_3) = -1.5$

Assuming the agent performs one optimization step to maximize the objective, how will the policy probabilities $\pi_{\theta}(a|s)$ for these actions most likely change?

Impact of a Zero Advantage Value

Policy Gradient with Advantage Function Formula

Rationale for Using the Advantage Function in Policy Gradients

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