A2C Actor Loss Function

Application of A2C in RLHF for LLM Alignment

Advantage Estimation for A2C with a Reward Model

In an actor-critic reinforcement learning algorithm, the policy $\pi_{\theta}(a|s)$ is updated to maximize the objective function $U(\theta) = \sum_{t} \log \pi_{\theta}(a_t|s_t)A(s_t, a_t)$, where $A(s_t, a_t)$ is the advantage of taking action $a_t$ in state $s_t$. If, for a specific state-action pair $(s_k, a_k)$, the calculated advantage $A(s_k, a_k)$ is a large positive value, what is the intended immediate effect on the policy after a gradient-based update step?

Analysis of a Policy Gradient Update

In an actor-critic reinforcement learning framework, the actor's objective is to adjust its policy parameters, $\theta$, to maximize the utility function $U(\theta) = \sum_{t} \log \pi_{\theta}(a_t|s_t)A(s_t, a_t)$. Consider the following statement: 'If the advantage function $A(s_t, a_t)$ for a specific action $a_t$ is negative, the optimization process will adjust the policy parameters to decrease the probability $\pi_{\theta}(a_t|s_t)$ of selecting that action in state $s_t$ in the future.'