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.

AI Agents for Rock Paper Scissors is an open-source Python project that demonstrates how to build, train, and evaluate different AI strategies—random play, rule-based pattern recognition, and reinforcement learning (Q-learning)—in the classic Rock-Paper-Scissors game. It provides modular agent classes, a configurable game runner, performance logging, and visualization utilities. Users can easily swap agents, adjust learning parameters, and explore AI behavior in competitive scenarios.