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Reward Models as the Basis for Value Functions
In the general framework of reinforcement learning, reward models hold a critical role as they establish the foundation upon which value functions are computed. The estimations from a reward model are essential for calculating the long-term value associated with particular states or actions.
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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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Reward Models as the Basis for Value Functions
An autonomous agent is being trained to navigate a maze and reach a specific exit. The agent receives a small negative feedback signal (-0.1) for every step it takes and a large positive feedback signal (+100) only when it reaches the correct exit. The agent's goal is to maximize its total feedback score. Given this feedback structure, what is the most likely reason the agent might fail to learn to solve the maze, even after many attempts?
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Learn After
Impact of Reward Model Flaws on Value Function Estimation
A reinforcement learning agent is trained to find the exit in a maze. Two reward models are proposed. Model A gives a reward of +100 for reaching the exit and 0 for every other step. Model B gives +100 for reaching the exit but also a -1 penalty for each step taken. How will the value function derived from Model B most likely differ from the one derived from Model A for states that are not the exit?
Diagnosing Undesirable Agent Behavior
In a reinforcement learning framework, it is possible to compute a meaningful long-term value function for a policy even if the reward model consistently provides random, uninformative feedback for every action.