MultiAgent Image Classification via Reinforcement Learning We investigate a classification problem using multiple mobile agents capable of collecting (partial) posedependent observations of an unknown environment. The objective is to classify an image over a finite time horizon. We propose a network architecture on how agents should form a local belief, take local actions, and extract relevant features from their raw partial observations. Agents are allowed to exchange information with their neighboring agents to update their own beliefs. It is shown how reinforcement learning techniques can be utilized to achieve decentralized implementation of the classification problem by running a decentralized consensus protocol. Our experimental results on the MNIST handwritten digit dataset demonstrates the effectiveness of our proposed framework.

Policy Distillation and Value Matching in Multiagent Reinforcement Learning Multiagent reinforcement learning (MARL) algorithms have been demonstrated on complex tasks that require the coordination of a team of multiple agents to complete. Existing works have focused on sharing information between agents via centralized critics to stabilize learning or through communication to improve performance, but do not generally consider how information can be shared between agents to address the curse of dimensionality in MARL. We posit that a multiagent problem can be decomposed into a multitask problem where each agent explores a subset of the state space instead of exploring the entire state space. This paper introduces a multiagent actorcritic algorithm for combining knowledge from homogeneous agents through distillation and valuematching that outperforms policy distillation alone and allows further learning in discrete and continuous action spaces.