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Found 13 papers, 7 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.
Transformers are Efficient In-Context Estimators for Wireless Communication
A communication channel is essentially a noisy function that maps transmitted symbols to received symbols, and this function can be represented by an unknown parameter whose statistics depend on an (also unknown) latent context.
LLMZip: Lossless Text Compression using Large Language Models
We provide new estimates of an asymptotic upper bound on the entropy of English using the large language model LLaMA-7B as a predictor for the next token given a window of past tokens.
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.
Energy System Digitization in the Era of AI: A Three-Layered Approach towards Carbon Neutrality
The transition towards carbon-neutral electricity is one of the biggest game changers in addressing climate change since it addresses the dual challenges of removing carbon emissions from the two largest sectors of emitters: electricity and transportation.
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.
OpenGridGym: An Open-Source AI-Friendly Toolkit for Distribution Market Simulation
This paper presents OpenGridGym, an open-source Python-based package that allows for seamless integration of distribution market simulation with state-of-the-art artificial intelligence (AI) decision-making algorithms.
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.
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.
NeurWIN: Neural Whittle Index Network For Restless Bandits Via Deep RL
This paper proposes NeurWIN, a neural Whittle index network that seeks to learn the Whittle indices for any restless bandits by leveraging mathematical properties of the Whittle indices.
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.
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.
