Towards the Integration of Reinforcement Learning into MASPY - Mellado, Alexandre L. L.; Borges, André Pinz; Cardoso, Rafael C.; Alves, Gleifer Vaz (Anais do Workshop-Escola de Sistemas de Agentes, seus Ambientes e Aplicações (WESAAC); 2025: Anais do XIX Workshop-Escola de Sistemas de Agentes, seus Ambientes e Aplicações, 2025)
Learning in symbolic agent architectures remains a key challenge in the development of adaptive multi-agent systems. This paper introduces a learning module for MASPY, a Python-based framework inspired by the Belief-Desire-Intention (BDI) model. The module enables agents to learn optimal actions using tabular reinforcement learning algorithms, such as Q-Learning and SARSA. To support this, we propose the SART methodology, which decomposes the learning environment into four structured components: States, Actions, Rewards, and Transitions. This structure allows MASPY agents to perceive their environment through defined percepts, act through decorated functions, and adapt over time using discrete learning strategies. The learning module offers a unified Python-based architecture for symbolic reasoning agents that learn through reinforcement training. This is shown practically with a toy problem where agents are able to learn to execute the actions of a previously unknown environment.