Learn Before
Validating a Potential-Based Shaping Function
An AI developer is implementing a reward shaping function, f, to guide a reinforcement learning agent. They have defined a potential function, Φ(s), which estimates the value of any given state s, and are using a discount factor γ. They are considering three different formulas for the shaping reward based on a transition from state s_t to s_{t+1}:
f = Φ(s_{t+1}) - Φ(s_t)f = γΦ(s_{t+1}) - Φ(s_t)f = Φ(s_{t+1})
Which of these formulas should the developer choose to ensure that the agent's optimal policy is not altered by the additional reward? Justify your choice by explaining why it is correct and why the other two are not.
0
1
Tags
Ch.4 Alignment - Foundations of Large Language Models
Foundations of Large Language Models
Foundations of Large Language Models Course
Computing Sciences
Analysis in Bloom's Taxonomy
Cognitive Psychology
Psychology
Social Science
Empirical Science
Science
Related
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