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Found 12 papers, 1 papers with code
A Chain-of-Thought Prompting Approach with LLMs for Evaluating Students' Formative Assessment Responses in Science
This paper explores the use of large language models (LLMs) to score and explain short-answer assessments in K-12 science.
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.
Comparison of Model Predictive and Reinforcement Learning Methods for Fault Tolerant Control
A desirable property in fault-tolerant controllers is adaptability to system changes as they evolve during systems operations.
Hierarchical Reinforcement Learning
reinforcement-learning
+1
Fault-Tolerant Control of Degrading Systems with On-Policy Reinforcement Learning
We propose a novel adaptive reinforcement learning control approach for fault tolerant control of degrading systems that is not preceded by a fault detection and diagnosis step.
A Relearning Approach to Reinforcement Learning for Control of Smart Buildings
We demonstrate this approach for a data-driven 'smart building environment' that we use as a test-bed for developing HVAC controllers for reducing energy consumption of large buildings on our university campus.
Using the Dempster-Shafer Scheme in a Diagnostic Expert System Shell
This paper discusses an expert system shell that integrates rule-based reasoning and the Dempster-Shafer evidence combination scheme.
A CONTEXTUALIZED, DIFFERENTIAL SEQUENCE MINING METHOD TO DERIVE STUDENTS' LEARNING BEHAVIOR PATTERNS
Computer-based learning environments can produce a wealth of data on student learning interactions.
