Critic Network Loss in A2C
In the Advantage Actor-Critic (A2C) algorithm, the critic network (or value network) is trained using a specific loss function. This loss is generally formulated as the mean squared error between the computed return, , and the predicted state value, . The training process adjusts the critic network's parameters, denoted by , to minimize this error, thereby improving its evaluation of the policy.
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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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Critic Network Loss in A2C
Training the Value Function with a Reward Model
In an actor-critic learning process, an agent is being trained. It is observed that the agent repeatedly takes actions that lead to states with poor long-term outcomes. Assuming the action-selection mechanism is functioning correctly based on its inputs, which of the following describes the most probable malfunction in the state-value estimation component that would cause this behavior?
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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