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Found 6 papers, 1 papers with code
MARVEL: Multi-Agent Reinforcement-Learning for Large-Scale Variable Speed Limits
Variable Speed Limit (VSL) control acts as a promising highway traffic management strategy with worldwide deployment, which can enhance traffic safety by dynamically adjusting speed limits according to real-time traffic conditions.
A Reinforcement Learning Approach for Robust Supervisory Control of UAVs Under Disturbances
In this work, we present an approach to supervisory reinforcement learning control for unmanned aerial vehicles (UAVs).
Model-based adaptation for sample efficient transfer in reinforcement learning control of parameter-varying systems
Instead, we propose a model-based transformation, such that when actions from a control policy are applied to the target system, a positive transfer is achieved.
Concurrent Policy Blending and System Identification for Generalized Assistive Control
In this work, we address the problem of solving complex collaborative robotic tasks subject to multiple varying parameters.
Performance-Weighed Policy Sampling for Meta-Reinforcement Learning
The enhancement is applied when a new fault occurs, to re-initialize the parameters of a new RL policy that achieves faster adaption with a small number of samples of system behavior with the new fault.
Complementary Meta-Reinforcement Learning for Fault-Adaptive Control
This contrasts with MAML, where the controller derives intermediate policies anew, sampled from a distribution of similar systems, to initialize a new policy.
