Reward Advancement: Transforming Policy under Maximum Causal Entropy Principle
This work addresses policy transformation for human-like sequential decision-making, but it appears incremental as it builds on existing maximum causal entropy frameworks.
The paper tackles the problem of transforming an agent's policy to a predefined target policy under the maximum causal entropy principle by recovering additional reward functions, showing that infinite such functions exist and proposing an algorithm to extract those with minimum implementation cost.
Many real-world human behaviors can be characterized as a sequential decision making processes, such as urban travelers choices of transport modes and routes (Wu et al. 2017). Differing from choices controlled by machines, which in general follows perfect rationality to adopt the policy with the highest reward, studies have revealed that human agents make sub-optimal decisions under bounded rationality (Tao, Rohde, and Corcoran 2014). Such behaviors can be modeled using maximum causal entropy (MCE) principle (Ziebart 2010). In this paper, we define and investigate a general reward trans-formation problem (namely, reward advancement): Recovering the range of additional reward functions that transform the agent's policy from original policy to a predefined target policy under MCE principle. We show that given an MDP and a target policy, there are infinite many additional reward functions that can achieve the desired policy transformation. Moreover, we propose an algorithm to further extract the additional rewards with minimum "cost" to implement the policy transformation.