Search papers, labs, and topics across Lattice.
This paper introduces a free energy-based social bandit learning algorithm that enables an agent to learn from the actions of other agents without observing their rewards or knowing their expertise levels. The algorithm estimates the expertise of other agents by evaluating their policies and integrates this information with its own experiences to improve learning outcomes. Theoretical analysis proves the algorithm's convergence to the optimal policy, and empirical results demonstrate its superiority over other social learning methods, even in the presence of non-expert or suboptimal agents, while maintaining logarithmic regret.
Learning from others doesn't require knowing who's an expert: this social bandit algorithm figures it out and improves performance even with non-experts in the mix.
Personalized AI-based services involve a population of individual reinforcement learning agents. However, most reinforcement learning algorithms focus on harnessing individual learning and fail to leverage the social learning capabilities commonly exhibited by humans and animals. Social learning integrates individual experience with observing others'behavior, presenting opportunities for improved learning outcomes. In this study, we focus on a social bandit learning scenario where a social agent observes other agents'actions without knowledge of their rewards. The agents independently pursue their own policy without explicit motivation to teach each other. We propose a free energy-based social bandit learning algorithm over the policy space, where the social agent evaluates others'expertise levels without resorting to any oracle or social norms. Accordingly, the social agent integrates its direct experiences in the environment and others'estimated policies. The theoretical convergence of our algorithm to the optimal policy is proven. Empirical evaluations validate the superiority of our social learning method over alternative approaches in various scenarios. Our algorithm strategically identifies the relevant agents, even in the presence of random or suboptimal agents, and skillfully exploits their behavioral information. In addition to societies including expert agents, in the presence of relevant but non-expert agents, our algorithm significantly enhances individual learning performance, where most related methods fail. Importantly, it also maintains logarithmic regret.
