Q-Learning
388 papers with code • 0 benchmarks • 2 datasets
The goal of Q-learning is to learn a policy, which tells an agent what action to take under what circumstances.
( Image credit: Playing Atari with Deep Reinforcement Learning )
Benchmarks
These leaderboards are used to track progress in Q-Learning
Libraries
Use these libraries to find Q-Learning models and implementations
VizDoom
Yeast
Latest papers with no code
Compressed Federated Reinforcement Learning with a Generative Model
Addressing this challenge, federated reinforcement learning (FedRL) has emerged, wherein agents collaboratively learn a single policy by aggregating local estimations.
Semantic-Aware Remote Estimation of Multiple Markov Sources Under Constraints
This paper studies semantic-aware communication for remote estimation of multiple Markov sources over a lossy and rate-constrained channel.
DASA: Delay-Adaptive Multi-Agent Stochastic Approximation
We consider a setting in which $N$ agents aim to speedup a common Stochastic Approximation (SA) problem by acting in parallel and communicating with a central server.
A Fairness-Oriented Reinforcement Learning Approach for the Operation and Control of Shared Micromobility Services
As Machine Learning systems become increasingly popular across diverse application domains, including those with direct human implications, the imperative of equity and algorithmic fairness has risen to prominence in the Artificial Intelligence community.
Reinforcement Learning for Online Testing of Autonomous Driving Systems: a Replication and Extension Study
Our extension aims at eliminating some of the possible reasons for the poor performance of RL observed in our replication: (1) the presence of reward components providing contrasting or useless feedback to the RL agent; (2) the usage of an RL algorithm (Q-learning) which requires discretization of an intrinsically continuous state space.
State-Separated SARSA: A Practical Sequential Decision-Making Algorithm with Recovering Rewards
While many multi-armed bandit algorithms assume that rewards for all arms are constant across rounds, this assumption does not hold in many real-world scenarios.
Neural-Kernel Conditional Mean Embeddings
In conditional density estimation tasks, our NN-CME hybrid achieves competitive performance and often surpasses existing deep learning-based methods.
A Reinforcement Learning Approach to Dairy Farm Battery Management using Q Learning
This study proposes a Q-learning-based algorithm for scheduling battery charging and discharging in a dairy farm setting.
Strategizing against Q-learners: A Control-theoretical Approach
In this paper, we explore the susceptibility of the Q-learning algorithm (a classical and widely used reinforcement learning method) to strategic manipulation of sophisticated opponents in games.
Model-free Resilient Controller Design based on Incentive Feedback Stackelberg Game and Q-learning
In the swift evolution of Cyber-Physical Systems (CPSs) within intelligent environments, especially in the industrial domain shaped by Industry 4. 0, the surge in development brings forth unprecedented security challenges.
