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In the context of the Advantage Actor-Critic (A2C) algorithm, the advantage function $A(s_t, a_t)$ that appears in the utility function is typically estimated using the Temporal Difference (TD) error, calculated as $r_t + \gamma V(s_{t+1}) - V(s_t)$. The value function $V(s_t)$ used in this estimation is, in turn, trained with a reward model.

Advantage Estimation for A2C with a Reward Model

An agent in a reinforcement learning system takes an action in a given state, resulting in a transition to a new state. A value network provides the following estimates for the states: the value of the current state is 2.5, and the value of the next state is 3.0. A separate reward model provides an immediate reward of 0.5 for this transition. Assuming a discount factor of 0.9, calculate the one-step temporal difference error used as an estimate for the advantage of the action taken. Show your calculation.

Calculating Advantage Estimate

Analyze the following scenario and identify the most likely reason for the observed training issue. Explain your reasoning by first calculating the advantage estimate and then interpreting its components.

Debugging Advantage Estimation in A2C

An actor-critic agent is being trained to perform a task where explicit rewards are not available from the environment. Instead, a separate, pre-trained reward model provides a scalar reward `r_t` for each transition `(s_t, a_t, s_{t+1})`. The agent also maintains a value network that estimates the expected future return from any given state, `V(s)`. Given a discount factor `γ`, which of the following correctly represents the one-step temporal difference (TD) error used to estimate the advantage

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