Search Results for author:
Found 12 papers, 2 papers with code
Safe and Balanced: A Framework for Constrained Multi-Objective Reinforcement Learning
In numerous reinforcement learning (RL) problems involving safety-critical systems, a key challenge lies in balancing multiple objectives while simultaneously meeting all stringent safety constraints.
A CMDP-within-online framework for Meta-Safe Reinforcement Learning
Meta-reinforcement learning has widely been used as a learning-to-learn framework to solve unseen tasks with limited experience.
Balance Reward and Safety Optimization for Safe Reinforcement Learning: A Perspective of Gradient Manipulation
Ensuring the safety of Reinforcement Learning (RL) is crucial for its deployment in real-world applications.
DyBit: Dynamic Bit-Precision Numbers for Efficient Quantized Neural Network Inference
To accelerate the inference of deep neural networks (DNNs), quantization with low-bitwidth numbers is actively researched.
Scalable Multi-Agent Reinforcement Learning with General Utilities
The objective is to find a localized policy that maximizes the average of the team's local utility functions without the full observability of each agent in the team.
Multi-agent Reinforcement Learning
reinforcement-learning
+1
Non-stationary Risk-sensitive Reinforcement Learning: Near-optimal Dynamic Regret, Adaptive Detection, and Separation Design
We study risk-sensitive reinforcement learning (RL) based on an entropic risk measure in episodic non-stationary Markov decision processes (MDPs).
Policy-based Primal-Dual Methods for Concave CMDP with Variance Reduction
In the exact setting, we prove an $O(T^{-1/3})$ convergence rate for both the average optimality gap and constraint violation, which further improves to $O(T^{-1/2})$ under strong concavity of the objective in the occupancy measure.
Provably Efficient Primal-Dual Reinforcement Learning for CMDPs with Non-stationary Objectives and Constraints
We consider primal-dual-based reinforcement learning (RL) in episodic constrained Markov decision processes (CMDPs) with non-stationary objectives and constraints, which plays a central role in ensuring the safety of RL in time-varying environments.
Beyond Exact Gradients: Convergence of Stochastic Soft-Max Policy Gradient Methods with Entropy Regularization
Our result is the first global convergence and sample complexity results for the stochastic entropy-regularized vanilla PG method.
On the Global Optimum Convergence of Momentum-based Policy Gradient
For the generic Fisher-non-degenerate policy parametrizations, our result is the first single-loop and finite-batch PG algorithm achieving $\tilde{O}(\epsilon^{-3})$ global optimality sample complexity.
A Dual Approach to Constrained Markov Decision Processes with Entropy Regularization
We study entropy-regularized constrained Markov decision processes (CMDPs) under the soft-max parameterization, in which an agent aims to maximize the entropy-regularized value function while satisfying constraints on the expected total utility.
Ontology-Enhanced Slot Filling
In multi-domain task-oriented dialog system, user utterances and system responses may mention multiple named entities and attributes values.
