Perimeter metering control has long been an active research topic since well-defined relationships between network productivity and usage, that is, network macroscopic fundamental diagrams (MFDs), were shown to be capable of describing regional traffic dynamics. Numerous methods have been proposed to solve perimeter metering control problems, but these generally require knowledge of the MFDs or detailed equations that govern traffic dynamics. Recently, a study applied model-free deep reinforcement learning (Deep-RL) methods to two-region perimeter control and found comparable performances to the model predictive control scheme, particularly when uncertainty exists. However, the proposed methods therein provide very low initial performances during the learning process, which limits their applicability to real life scenarios. Furthermore, the methods may not be scalable to more complicated networks with larger state and action spaces. To combat these issues, this paper proposes to integrate the domain control knowledge (DCK) of congestion dynamics into the agent designs for improved learning and control performances. A novel agent is also developed that builds on the Bang-Bang control policy. Two types of DCK are then presented to provide knowledge-guided exploration strategies for the agents such that they can explore around the most rewarding part of the action spaces. The results from extensive numerical experiments on two- and three-region urban networks show that integrating DCK can (a) effectively improve learning and control performances for Deep-RL agents, (b) enhance the agents’ resilience against various types of environment uncertainties, and (c) mitigate the scalability issue for the agents.
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This content will become publicly available on July 1, 2024
RLang: A Declarative Language for Describing Partial World Knowledge to Reinforcement Learning Agents
We introduce RLang, a domain-specific language (DSL) for communicating domain knowledge to
an RL agent. Unlike existing RL DSLs that ground to single elements of a decision-making formalism (e.g., the reward function or policy), RLang can specify information about every element of a Markov decision process. We define precise syntax and grounding semantics for RLang, and provide a parser that grounds RLang programs to an algorithm-agnostic partial world model and policy that can be exploited by an RL
agent. We provide a series of example RLang programs demonstrating how different RL methods can exploit the resulting knowledge, encompassing model-free and model-based tabular algorithms, policy gradient and value-based methods, hierarchical approaches, and deep methods.
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- NSF-PAR ID:
- 10467320
- Publisher / Repository:
- Proceedings of the 40th International Conference on Machine Learning
- Date Published:
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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