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Found 7 papers, 0 papers with code
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.
Answer Fast: Accelerating BERT on the Tensor Streaming Processor
Transformers have become a predominant machine learning workload, they are not only the de-facto standard for natural language processing tasks, but they are also being deployed in other domains such as vision and speech recognition.
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.
