Search Results for author:
Found 7 papers, 3 papers with code
Structured Reinforcement Learning for Media Streaming at the Wireless Edge
The goal of this work is to develop and demonstrate learning-based policies for optimal decision making to determine which clients to dynamically prioritize in a video streaming setting.
N-Critics: Self-Refinement of Large Language Models with Ensemble of Critics
We propose a self-correction mechanism for Large Language Models (LLMs) to mitigate issues such as toxicity and fact hallucination.
Federated Ensemble-Directed Offline Reinforcement Learning
We consider the problem of federated offline reinforcement learning (RL), a scenario under which distributed learning agents must collaboratively learn a high-quality control policy only using small pre-collected datasets generated according to different unknown behavior policies.
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.
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.
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.
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.
