Theoretical and empirical analysis of this paper reveals important properties of this principle, especially the influence of the reward type, MDP discount ...
Dec 13, 2009 · Potential-based reward shaping has been shown to be a powerful method to improve the convergence rate of reinforcement learning agents. It is a ...
Reinforcement learning suffers scalability problems due to the state space explosion and the temporal credit assign- ment problem.
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Two popular shaping methods are Potential-Based Reward. Shaping and difference rewards, and both have been shown to improve learning speed and the quality of ...
10 Reward Shaping – Stochastic Control and Decision Theory
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The idea of reward shaping was proposed by Skinner (1938) to synthesize complex behavior by guiding animals to perform simple functions.
Applying conventional reinforcement to complex domains requires the use of an overly simplified task model, or a large amount of training experience.
A complete theory for the process of reward shaping is proposed that demonstrates how it accelerates learning, what the ideal shaping rewards are like, ...
Oct 18, 2022 · In this work, we take a step towards studying the effect of reward shaping on the efficiency of RL algorithms, by asking the following question:.
This chapter provides background information on reinforcement learning, defines the process of reward shaping, and outlines the contributions of this research.
Two popular shaping methods are Potential-Based Reward Shaping and difference rewards, and both have been shown to improve learning speed and the quality of ...