Batch Size for Sequential Data in A2C Value Loss

An agent is being trained using a reinforcement learning algorithm where the value network's loss is based on the mean squared temporal difference (TD) error. For a single transition, the agent moves from state s_t to s_{t+1}, receiving a reward r_t. The value network predicts the value of the current state as V(s_t) and the next state as V(s_{t+1}). Given the following values, calculate the squared TD error, which represents the loss for this single sample before averaging:

• Reward r_t = 2
• Discount factor γ = 0.9
• Predicted value of current state V(s_t) = 5.0
• Predicted value of next state V(s_{t+1}) = 4.0

An agent is being trained using an algorithm where the value network's performance is measured by the mean squared difference between its predicted value for a state, V(s_t), and a computed target value, r_t + γ * V(s_{t+1}). During a particular training batch, the network consistently produces predictions V(s_t) that are significantly lower than the computed target values. What is the most direct effect on the network's parameters during the subsequent optimization step?

Rationale for the Value Network Target