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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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Ch.4 Alignment - Foundations of Large Language Models
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Analysis in Bloom's Taxonomy
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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_tfor 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 factorγ, which of the following correctly represents the one-step temporal difference (TD) error used to estimate the advantageCalculating Advantage Estimate
Debugging Advantage Estimation in A2C