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Value Network Loss Function in A2C
In the Advantage Actor-Critic (A2C) algorithm, the loss function for the value network (or critic network), parameterized by , is defined as the mean squared temporal difference (TD) error over a batch of experiences. The formula is given by: Here, is the number of training samples (for example, for a sequence of tokens, we can set ). The term represents the computed return (TD target), and is the predicted state value. Minimizing this loss trains the critic to accurately evaluate the expected return.
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Ch.4 Alignment - Foundations of Large Language Models
Foundations of Large Language Models
Foundations of Large Language Models Course
Computing Sciences
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Value Network Loss Function in A2C
In a reinforcement learning agent using an actor-critic architecture, the critic network is being trained. For a given state transition, the network makes the following predictions:
- Predicted value for the current state: 15.0
- Predicted value for the next state: 20.0
The agent receives a reward of 5.0 for the transition, and the discount factor is 0.9.
Based on this single experience, how should the critic network's parameters be adjusted to minimize its loss?
Critic Network Training Target
Critic Network Performance Analysis
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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_ttos_{t+1}, receiving a rewardr_t. The value network predicts the value of the current state asV(s_t)and the next state asV(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
- Reward
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 predictionsV(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