Inverse Reinforcement Learning Through Max-Margin Algorithm

Reinforcement Learning (RL) methods provide a solution for decision-making problems under uncertainty. An agent finds a suitable policy through a reward function by interacting with a dynamic environment. However, for complex and large problems it is very difficult to specify and tune the reward function. Inverse Reinforcement Learning (IRL) may mitigate this problem by learning the reward function through expert demonstrations. This work exploits an IRL method named Max-Margin Algorithm (MMA) to learn the reward function for a robotic navigation problem. The learned reward function reveals the demonstrated policy (expert policy) better than all other policies. Results show that this method has better convergence and learned reward functions through the adopted method represents expert behavior more efficiently

A Norm Compliance Approach for Open and Goal-Directed Intelligent Systems

The increasing development of autonomous intelligent systems, such as smart vehicles, smart homes, and social robots, poses new challenges to face. Among them, ensuring that such systems behave lawfully is one of the crucial topics to be addressed for improving their employment in real contexts of daily life. In this work, we present an approach for norm compliance in the context of open and goal-directed intelligent systems working in dynamic normative environments where goals, services, and norms may change. Such an approach complements a goal-directed system modifying its goals and the way to achieve them for taking norms into accounts, thus influencing the practical reasoning process that goal-oriented systems implement for figuring out what to do. The conformity to norms is established at the goal level rather than at the action level. The effect of a norm that acts at the goal level spreads out at the lower level of actions, thus also improving system flexibility. Recovery mechanisms are provided to face exceptional situations that could be caused by normative changes. A case study in the field of the business organizations is presented for demonstrating the strengths of the proposed solution