[3Win5] Poster session 3

[3Win5-04] Evaluating Policy Diversity of Generative Models

〇Shori Muto¹, Takumi Suzuki², Tatsuji Takahashi¹, Yu Kono¹ (1.Tokyo Denki University, 2.Graduate School of Tokyo Denki University)

Keywords:Generative Model, Reinforcement Learning, Machine Learning

The performance of generative models cannot be evaluated solely by error or accuracy because output diversity, that is, diversity of images or language expressions conditioned on latent variables, is also essential. As generative models evolved, so have diversity metrics. While generative models have been trained on various modalities, progress in generative models for policies-action functions conditioned on states that represent real-world interactions-has been limited. Recently, models that embed action intentions as latent variables to generate policies were proposed, but we have no metrics to evaluate their diversity. Applying diversity metrics from other modalities is challenging because these models generate policies that map state inputs to action outputs, which prevents us from applying traditional methods straightforwardly. To address this, we propose a method that indirectly evaluates diversity using state trajectories generated from interactions between policies and the environment. Using this method, we evaluate the diversity of policies generated in a toy task to compare the performance of different policy generative models under varying parameters and architectures.