jason-RL adds a reinforcement learning layer to the Jason multi-agent framework, allowing AgentSpeak BDI agents to learn action-selection policies via reward feedback. It implements Q-learning and SARSA algorithms, supports configuration of learning parameters (learning rate, discount factor, exploration strategy), and logs training metrics. By defining reward functions in agent plans and running simulations, developers can observe agents improve decision making over time, adapting to changing environments without manual policy coding.

Joypanner is an innovative AI guide designed to support children in their learning journey. Instead of providing immediate answers, it prompts kids to think critically by asking follow-up questions. At the end of the engagement, it recommends curated videos and books to complement their learning. This platform is an excellent tool for parents and educators aiming to foster independent, curiosity-driven learning in children.