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Found 9 papers, 2 papers with code
Safe Hybrid-Action Reinforcement Learning-Based Decision and Control for Discretionary Lane Change
We introduce safe hybrid-action reinforcement learning into discretionary lane change for the first time and propose Parameterized Soft Actor-Critic with PID Lagrangian (PASAC-PIDLag) algorithm.
Robust Fully-Asynchronous Methods for Distributed Training over General Architecture
Perfect synchronization in distributed machine learning problems is inefficient and even impossible due to the existence of latency, package losses and stragglers.
Plug-in Hybrid Electric Vehicle Energy Management with Clutch Engagement Control via Continuous-Discrete Reinforcement Learning
The energy management of certain series-parallel PHEVs involves the control of continuous variables, such as engine torque, and discrete variables, such as clutch engagement/disengagement.
On the Computation-Communication Trade-Off with A Flexible Gradient Tracking Approach
We propose a flexible gradient tracking approach with adjustable computation and communication steps for solving distributed stochastic optimization problem over networks.
Mixed-Integer Optimal Control via Reinforcement Learning: A Case Study on Hybrid Vehicle Energy Management
Such problems are usually formulated as mixed-integer optimal control (MIOC) problems, which are challenging to solve due to the complexity of the solution space.
Tackling Data Heterogeneity: A New Unified Framework for Decentralized SGD with Sample-induced Topology
We develop a general framework unifying several gradient-based stochastic optimization methods for empirical risk minimization problems both in centralized and distributed scenarios.
Lithography Hotspot Detection via Heterogeneous Federated Learning with Local Adaptation
Moreover, the design houses are also unwilling to directly share such data with the other houses to build a unified model, which can be ineffective for the design house with unique design patterns due to data insufficiency.
Decentralized Coordination Between Economic Dispatch and Demand Response in Multi-Energy Systems
In this paper, we investigate the problem of coor? dination between economic dispatch (ED) and demand response (DR) in multi-energy systems (MESs), aiming to improve the economic utility and reduce the waste of energy in MESs.
Accelerated Primal-Dual Algorithms for Distributed Smooth Convex Optimization over Networks
This paper proposes a novel family of primal-dual-based distributed algorithms for smooth, convex, multi-agent optimization over networks that uses only gradient information and gossip communications.
