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Developing an Adaptive Strategy for Connected Eco-Driving Under Uncertain Traffic and Signal Conditions
The Eco-Approach and Departure (EAD) application has been proved to be environmentally efficient for a Connected and Automated Vehicles (CAVs) system. In the real-world traffic, traffic conditions and signal timings are usually dynamic and uncertain due to mixed vehicle types, various driving behaviors and limited sensing range, which is challenging in EAD development. This research proposes an adaptive strategy for connected eco-driving towards a signalized intersection under real world conditions. Stochastic graph models are built to link the vehicle and external (e.g., traffic, signal) data and dynamic programing is applied to identify the optimal speed for each vehicle-state efficiently. From energy perspective, adaptive strategy using traffic data could double the effective sensor range in eco-driving. A hybrid reinforcement learning framework is also developed for EAD in mixed traffic condition using both short-term benefit and long-term benefit as the action reward. Micro-simulation is conducted in Unity to validate the method, showing over 20% energy saving.View the NCST Project Webpag
Multi-Agent Deep Reinforcement Learning with Human Strategies
Deep learning has enabled traditional reinforcement learning methods to deal
with high-dimensional problems. However, one of the disadvantages of deep
reinforcement learning methods is the limited exploration capacity of learning
agents. In this paper, we introduce an approach that integrates human
strategies to increase the exploration capacity of multiple deep reinforcement
learning agents. We also report the development of our own multi-agent
environment called Multiple Tank Defence to simulate the proposed approach. The
results show the significant performance improvement of multiple agents that
have learned cooperatively with human strategies. This implies that there is a
critical need for human intellect teamed with machines to solve complex
problems. In addition, the success of this simulation indicates that our
multi-agent environment can be used as a testbed platform to develop and
validate other multi-agent control algorithms.Comment: 2019 IEEE International Conference on Industrial Technology (ICIT),
Melbourne, Australi
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