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Reinforcement Learning (RL) is an area of growing interest in the field ofartificial intelligence due to its many notable applications in diverse fields.Particularly within the context of intelligent vehicle control, RL has madeimpressive progress. However, currently it is still in simulated controlledenvironments where RL can achieve its full super-human potential. Although howto apply simulation experience in real scenarios has been studied, how toapproximate simulated problems to the real dynamic problems is still achallenge. In this paper, we discuss two methods that approximate RL problemsto real problems. In the context of traffic junction simulations, wedemonstrate that, if we can decompose a complex task into multiple sub-tasks,solving these tasks first can be advantageous to help minimising possibleoccurrences of catastrophic events in the complex task. From a multi-agentperspective, we introduce a training structuring mechanism that exploits theuse of experience learned under the popular paradigm called CentralisedTraining Decentralised Execution (CTDE). This experience can then be leveragedin fully decentralised settings that are conceptually closer to real settings,where agents often do not have access to a central oracle and must be treatedas isolated independent units. The results show that the proposed approachesimprove agents performance in complex tasks related to traffic junctions,minimising potential safety-critical problems that might happen in thesescenarios. Although still in simulation, the investigated situations areconceptually closer to real scenarios and thus, with these results, we intendto motivate further research in the subject.

Staged Reinforcement Learning for Complex Tasks through Decomposed Environments