On Generalization and Distributional Update for Mimicking Observations with Adequate Exploration
arXiv:2501.12785v1 Announce Type: new
Abstract: This paper tackles the efficiency and stability issues in learning from observations (LfO). We commence by investigating how reward functions and policies generalize in LfO. Subsequently, the built-in reinforcement learning (RL) approach in generative adversarial imitation from observation (GAIfO) is replaced with distributional soft actor-critic (DSAC). This change results in a novel algorithm called Mimicking Observations through Distributional Update Learning with adequate Exploration (MODULE), which combines soft actor-critic’s superior efficiency with distributional RL’s robust stability.
Abstract: This paper tackles the efficiency and stability issues in learning from observations (LfO). We commence by investigating how reward functions and policies generalize in LfO. Subsequently, the built-in reinforcement learning (RL) approach in generative adversarial imitation from observation (GAIfO) is replaced with distributional soft actor-critic (DSAC). This change results in a novel algorithm called Mimicking Observations through Distributional Update Learning with adequate Exploration (MODULE), which combines soft actor-critic’s superior efficiency with distributional RL’s robust stability.
Yirui Zhou, Xiaowei Liu, Xiaofeng Zhang, Yangchun Zhang
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