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Found 9 papers, 2 papers with code
Enhancing Efficiency of Safe Reinforcement Learning via Sample Manipulation
Safe reinforcement learning (RL) is crucial for deploying RL agents in real-world applications, as it aims to maximize long-term rewards while satisfying safety constraints.
Active Reinforcement Learning for Robust Building Control
Reinforcement learning (RL) is a powerful tool for optimal control that has found great success in Atari games, the game of Go, robotic control, and building optimization.
Adapting Surprise Minimizing Reinforcement Learning Techniques for Transactive Control
Optimizing prices for energy demand response requires a flexible controller with ability to navigate complex environments.
Offline-Online Reinforcement Learning for Energy Pricing in Office Demand Response: Lowering Energy and Data Costs
Our team is proposing to run a full-scale energy demand response experiment in an office building.
Using Meta Reinforcement Learning to Bridge the Gap between Simulation and Experiment in Energy Demand Response
Our team is proposing to run a full-scale energy demand response experiment in an office building.
Towards Efficient Data Valuation Based on the Shapley Value
In this paper, we study the problem of data valuation by utilizing the Shapley value, a popular notion of value which originated in cooperative game theory.
One Bit Matters: Understanding Adversarial Examples as the Abuse of Redundancy
Despite the great success achieved in machine learning (ML), adversarial examples have caused concerns with regards to its trustworthiness: A small perturbation of an input results in an arbitrary failure of an otherwise seemingly well-trained ML model.
A Deep Learning and Gamification Approach to Energy Conservation at Nanyang Technological University
We propose the design and implementation of a large-scale network game with the goal of improving the energy efficiency of a building through the utilization of cutting-edge Internet of Things (IoT) sensors and cyber-physical systems sensing/actuation platforms.
Inverse Reinforcement Learning via Deep Gaussian Process
We propose a new approach to inverse reinforcement learning (IRL) based on the deep Gaussian process (deep GP) model, which is capable of learning complicated reward structures with few demonstrations.
