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Found 31 papers, 9 papers with code
Improved Sample Complexity Bounds for Distributionally Robust Reinforcement Learning
We formulate this as a distributionally robust reinforcement learning (DR-RL) problem where the objective is to learn the policy which maximizes the value function against the worst possible stochastic model of the environment in an uncertainty set.
Dynamic Regret Analysis of Safe Distributed Online Optimization for Convex and Non-convex Problems
We prove that for convex functions, D-Safe-OGD achieves a dynamic regret bound of $O(T^{2/3} \sqrt{\log T} + T^{1/3}C_T^*)$, where $C_T^*$ denotes the path-length of the best minimizer sequence.
Optimal Control of Material Micro-Structures
In this paper, we consider the optimal control of material micro-structures.
Enhanced Meta Reinforcement Learning using Demonstrations in Sparse Reward Environments
Meta reinforcement learning (Meta-RL) is an approach wherein the experience gained from solving a variety of tasks is distilled into a meta-policy.
DETERRENT: Detecting Trojans using Reinforcement Learning
Insertion of hardware Trojans (HTs) in integrated circuits is a pernicious threat.
Meta-Learning Online Control for Linear Dynamical Systems
We show that when the number of tasks are sufficiently large, our proposed approach achieves a meta-regret that is smaller by a factor $D/D^{*}$ compared to an independent-learning online control algorithm which does not perform learning across the tasks, where $D$ is a problem constant and $D^{*}$ is a scalar that decreases with increase in the similarity between tasks.
Robust Reinforcement Learning using Offline Data
The goal of robust reinforcement learning (RL) is to learn a policy that is robust against the uncertainty in model parameters.
Distributed Learning of Neural Lyapunov Functions for Large-Scale Networked Dissipative Systems
One standard approach to estimate the stability region of a general nonlinear system is to first find a Lyapunov function for the system and characterize its region of attraction as the stability region.
Anchor-Changing Regularized Natural Policy Gradient for Multi-Objective Reinforcement Learning
We study policy optimization for Markov decision processes (MDPs) with multiple reward value functions, which are to be jointly optimized according to given criteria such as proportional fairness (smooth concave scalarization), hard constraints (constrained MDP), and max-min trade-off.
The Impact of Heavy-Duty Vehicle Electrification on Large Power Grids: a Synthetic Texas Case Study
In fact, we find that as little as 11% of heavy duty vehicles in Texas charging simultaneously can lead to significant voltage violations on the transmission network that compromise grid reliability.
Reinforcement Learning with Sparse Rewards using Guidance from Offline Demonstration
We demonstrate the superior performance of our algorithm over state-of-the-art approaches on a number of benchmark environments with sparse rewards and censored state.
Off-Policy Evaluation Using Information Borrowing and Context-Based Switching
We consider the off-policy evaluation (OPE) problem in contextual bandits, where the goal is to estimate the value of a target policy using the data collected by a logging policy.
Sample Complexity of Robust Reinforcement Learning with a Generative Model
For each of these uncertainty sets, we give a precise characterization of the sample complexity of our proposed algorithm.
Model-based Reinforcement Learning
reinforcement-learning
+1
DOPE: Doubly Optimistic and Pessimistic Exploration for Safe Reinforcement Learning
Safe reinforcement learning is extremely challenging--not only must the agent explore an unknown environment, it must do so while ensuring no safety constraint violations.
Online Learning for Predictive Control with Provable Regret Guarantees
Specifically, we study the online learning problem where the control algorithm does not know the true system model and has only access to a fixed-length (that does not grow with the control horizon) preview of the future cost functions.
Safe Online Convex Optimization with Unknown Linear Safety Constraints
We study the problem of safe online convex optimization, where the action at each time step must satisfy a set of linear safety constraints.
Policy Optimization for Constrained MDPs with Provable Fast Global Convergence
We propose a new algorithm called policy mirror descent-primal dual (PMD-PD) algorithm that can provably achieve a faster $\mathcal{O}(\log(T)/T)$ convergence rate for both the optimality gap and the constraint violation.
OTTR: Off-Road Trajectory Tracking using Reinforcement Learning
Compared to the standard ILQR approach, our proposed approach achieves a 30% and 50% reduction in cross track error in Warthog and Moose, respectively, by utilizing only 30 minutes of real-world driving data.
PyProD: A Machine Learning-Friendly Platform for Protection Analytics in Distribution Systems
This paper introduces PyProD, a Python-based machine learning (ML)-compatible test-bed for evaluating the efficacy of protection schemes in electric distribution grids.
Learning Policies with Zero or Bounded Constraint Violation for Constrained MDPs
We show that when a strictly safe policy is known, then one can confine the system to zero constraint violation with arbitrarily high probability while keeping the reward regret of order $\tilde{\mathcal{O}}(\sqrt{K})$.
Fully Decentralized Reinforcement Learning-based Control of Photovoltaics in Distribution Grids for Joint Provision of Real and Reactive Power
In this paper, we introduce a new framework to address the problem of voltage regulation in unbalanced distribution grids with deep photovoltaic penetration.
Learning with Safety Constraints: Sample Complexity of Reinforcement Learning for Constrained MDPs
Many physical systems have underlying safety considerations that require that the policy employed ensures the satisfaction of a set of constraints.
Reinforcement Learning for Mean Field Games with Strategic Complementarities
We introduce a natural refinement to the equilibrium concept that we call Trembling-Hand-Perfect MFE (T-MFE), which allows agents to employ a measure of randomization while accounting for the impact of such randomization on their payoffs.
Robust Reinforcement Learning using Least Squares Policy Iteration with Provable Performance Guarantees
We first propose the Robust Least Squares Policy Evaluation algorithm, which is a multi-step online model-free learning algorithm for policy evaluation.
Deep Reinforcement Learning-BasedRobust Protection in DER-Rich Distribution Grids
This paper introduces the concept of Deep Reinforcement Learning based architecture for protective relay design in power distribution systems with many distributed energy resources (DERs).
Bounded Regret for Finitely Parameterized Multi-Armed Bandits
We propose an algorithm that is simple and easy to implement, which we call Finitely Parameterized Upper Confidence Bound (FP-UCB) algorithm, which uses the information about the underlying parameter set for faster learning.
Decoupled Data Based Approach for Learning to Control Nonlinear Dynamical Systems
This paper proposes a novel decoupled data-based control (D2C) algorithm that addresses this problem using a decoupled, `open loop - closed loop', approach.
QFlow: A Learning Approach to High QoE Video Streaming at the Wireless Edge
The predominant use of wireless access networks is for media streaming applications, which are only gaining popularity as ever more devices become available for this purpose.
On Regret-Optimal Learning in Decentralized Multi-player Multi-armed Bandits
The objective is to design a policy that maximizes the expected reward over a time horizon for a single player setting and the sum of expected rewards for the multiplayer setting.
Empirical Q-Value Iteration
We propose a new simple and natural algorithm for learning the optimal Q-value function of a discounted-cost Markov Decision Process (MDP) when the transition kernels are unknown.
Approachability in Stackelberg Stochastic Games with Vector Costs
Firstly, we give a simple and computationally tractable strategy for approachability for Stackelberg stochastic games along the lines of Blackwell's.
