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Equivalence of Advantage Estimation and Reward Shaping
In reinforcement learning, an agent's policy is often updated using an estimate of the advantage function, calculated as r + γV(s_{t+1}) - V(s_t). Explain how this specific calculation can be interpreted as a form of reward shaping and identify the 'potential function' being used in this context.
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A reinforcement learning agent, in a state
s_twith an estimated valueV(s_t) = 50, takes an action. This action yields an immediate rewardr = 5and transitions the agent to a new states_{t+1}with an estimated valueV(s_{t+1}) = 40. Assuming a discount factorγ = 0.9, the agent's learning algorithm uses the quantityr + γV(s_{t+1}) - V(s_t)to update its policy. How should the agent interpret the outcome of this action?Explaining Accelerated Learning in Reinforcement Learning
Equivalence of Advantage Estimation and Reward Shaping
In reinforcement learning, using the one-step advantage estimate, calculated as
r + γV(s_{t+1}) - V(s_t), to update an agent's policy is a fundamentally distinct approach from training the agent with a shaped reward signal.