Traffic light control is one of the main means of controlling road traffic. Improving traffic control is important because it can lead to higher traffic throughput and reduced congestion. This chapter describes multiagent reinforcement learning techniques for automatic optimization of traffic light controllers. Such techniques are attractive because they can automatically discover efficient control strategies for complex tasks, such as traffic control, for which it is hard or impossible to compute optimal solutions directly and hard to develop hand-coded solutions. First the general multi-agent reinforcement learning framework is described that is used to control traffic lights in this work. In this framework, multiple local controllers (agents) are each responsible for the optimization of traffic lights around a single traffic junction, making use of locally perceived traffic state information (sensed cars on the road), a learned probabilistic model of car behavior, and a learned value function which indicates how traffic light decisions affect long-term utility, in terms of the average waiting time of cars. Next, three extensions are described which improve upon the basic framework in various ways: agents (traffic junction controllers) taking into account congestion information from neighboring agents; handling partial observability of traffic states; and coordinating the behavior of multiple agents by coordination graphs and the max-plus algorithm.
Recent progress in Artificial Intelligence, sensing and network technology, robotics, and (cloud) computing has enabled the development of intelligent autonomous machine systems. Telling such autonomous systems "what to do" in a responsible way, is a non-trivial task. For intelligent autonomous machines to function in human society and collaborate with humans, we see three challenges ahead affecting meaningful control of autonomous systems. First, autonomous machines are not yet capable of handling failures and unexpected situations. Providing procedures for all possible failures and situations is unfeasible because the state-action space would explode. Machines should therefore become self-aware (self-assessment, self-management) enabling them to handle unexpected situations when they arise. This is a challenge for the computer science community. Second, in order to keep (meaningful) control, humans come into a new role of providing intelligent autonomous machines with objectives or goal functions (including rules, norms, constraints and moral values), specifying the utility of every possible outcome of actions of autonomous machines. Third, in order to be able to collaborate with humans, autonomous systems will require an understanding of (us) humans (i.e., our social, cognitive, affective and physical behaviors) and the ability to engage in partnership interactions (such as explanations of task performances, and the establishment of joint goals and work agreements). These are new challenges for the cognitive ergonomics community. CCS CONCEPTS • Human-centered computing → Interaction design → Interaction design theory, concepts and paradigms;
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