Abstract
Multi-agent Reinforcement Learning is required to adapt to the dynamic of the environment by transferring the learning outcomes in the case of the non-communicative and dynamic environment. Profit minimizing reinforcement learning with the oblivion of memory (PMRL-OM) enables agents to learn a co-operative policy using learning dynamics instead of communication information. It enables the agents to adapt to the dynamics of the other agents’ behaviors without any design of the relationship or communication rules between agents. It helps easily to add robots to the system with keeping co-operation in a multi-robot system. However, it is available for long-term dynamic changes, but not for the short-them changes because it used the outcome with enough trials. This paper picked up cyclic environmental changes as short-term changes and aimed to improve the performance in cyclic environmental changes and analyze theoretically the rationality of this approach. Specifically, we extend PMRL-OM based on an analysis of the PMRL-OM approach. Our experiments evaluated the performance of the proposed method for a navigation task in a maze-type environment undergoing cyclic environmental change, with the results showing that the proposed method gave an enhanced performance. Our method also enabled the adaptation to cyclic change to occur sooner than for the existing PMRL-OM method. In addition, the theoretical analysis not only investigates the PMRL-OM rationality but also suggests optimal parameter values for the proposed method. The proposed method contributed to XAI by showing the precise profits of the agents and the approach with rationality.
This research was supported by JSPS KAKENHI Grant Number JP21K17807.
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Uwano, F., Takadama, K. (2023). Reinforcement Learning in Cyclic Environmental Changes for Agents in Non-Communicative Environments: A Theoretical Approach. In: Calvaresi, D., et al. Explainable and Transparent AI and Multi-Agent Systems. EXTRAAMAS 2023. Lecture Notes in Computer Science(), vol 14127. Springer, Cham. https://doi.org/10.1007/978-3-031-40878-6_9
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