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Explaining Accelerated Learning in Reinforcement Learning
Based on the relationship between the learning signal r + γV(s_{t+1}) - V(s_t) and the principles of reward shaping, analyze why using this signal often leads to more effective and faster learning compared to using the raw environmental reward r alone. Explain how this modified signal 'shapes' the agent's learning process.
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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.