4 research outputs found
Reinforcement Learning with Model Predictive Control for Highway Ramp Metering
In the backdrop of an increasingly pressing need for effective urban and
highway transportation systems, this work explores the synergy between
model-based and learning-based strategies to enhance traffic flow management by
use of an innovative approach to the problem of highway ramp metering control
that embeds Reinforcement Learning techniques within the Model Predictive
Control framework. The control problem is formulated as an RL task by crafting
a suitable stage cost function that is representative of the traffic
conditions, variability in the control action, and violations of a
safety-critical constraint on the maximum number of vehicles in queue. An
MPC-based RL approach, which merges the advantages of the two paradigms in
order to overcome the shortcomings of each framework, is proposed to learn to
efficiently control an on-ramp and to satisfy its constraints despite
uncertainties in the system model and variable demands. Finally, simulations
are performed on a benchmark from the literature consisting of a small-scale
highway network. Results show that, starting from an MPC controller that has an
imprecise model and is poorly tuned, the proposed methodology is able to
effectively learn to improve the control policy such that congestion in the
network is reduced and constraints are satisfied, yielding an improved
performance compared to the initial controller.Comment: 14 pages, 10 figures, 3 tables, submitted to IEEE Transactions on
Intelligent Transportation System
Source code for the publication: Learning safety in model-based Reinforcement Learning using MPC and Gaussian Processes
Source code for a safety-aware MPC-based RL framework via Gaussian Processes</p
Source code for the publication: Learning safety in model-based Reinforcement Learning using MPC and Gaussian Processes
Source code for a safety-aware MPC-based RL framework via Gaussian Processes</p
Source code for the publication: Learning safety in model-based Reinforcement Learning using MPC and Gaussian Processes
Source code for a safety-aware MPC-based RL framework via Gaussian Processes</p