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Value-Based Reward Shaping Formula
This formula presents a specific application of potential-based reward shaping where the state-value function, , is used as the potential function, . The transformed reward, , is calculated by augmenting the original environmental reward, , with a shaping term derived from the change in the discounted state value between the subsequent state, , and the current state, . The formula is expressed as:
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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-Based Reward Shaping Formula
A reinforcement learning engineer wants to add an extra reward signal, denoted as a function
f, to an agent's learning process to encourage more efficient exploration. They have access to a functionΦ(s)which provides a numerical estimate of a state's value, and a discount factorγ. To guarantee that this additional reward signal does not alter the agent's optimal long-term behavior, which of the following structures must the functionfhave for a transition from states_ttos_{t+1}?Analyzing a Flawed Reward Shaping Implementation
Validating a Potential-Based Shaping Function
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Advantage Function as a Form of Shaped Reward
Calculating a Shaped Reward
An agent is being trained using value-based reward shaping. In a particular transition from state
s_ttos_{t+1}, the agent receives an environmental rewardrof 0. The agent's current value function estimates that the value of the next state,V(s_{t+1}), is substantially higher than the value of the current state,V(s_t). Based on the formular' = r + γV(s_{t+1}) - V(s_t), what is the most likely consequence of this shaping on the agent's learning for this specific transition?Analyze the value-based reward shaping formula,
r' = r + γV(s_{t+1}) - V(s_t), by matching each component to its specific role or definition within the general structure of potential-based reward shaping.