SOTAVerified

Reinforcement Learning (RL)

Reinforcement Learning (RL) involves training an agent to take actions in an environment to maximize a cumulative reward signal. The agent interacts with the environment and learns by receiving feedback in the form of rewards or punishments for its actions. The goal of reinforcement learning is to find the optimal policy or decision-making strategy that maximizes the long-term reward.

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 10001–10025 of 15113 papers

TitleStatusHype
Intelligent Replication Management for HDFS Using Reinforcement Learning—0
Enhancing Graph Neural Network-based Fraud Detectors against Camouflaged FraudstersCode1
A Survey of Knowledge-based Sequential Decision Making under Uncertainty—0
Reinforcement Learning for Low-Thrust Trajectory Design of Interplanetary MissionsCode1
Adaptive trading strategies across liquidity pools—0
A Framework for Studying Reinforcement Learning and Sim-to-Real in Robot Soccer—0
Learning Fair Policies in Multiobjective (Deep) Reinforcement Learning with Average and Discounted RewardsCode0
Communicative Reinforcement Learning Agents for Landmark Detection in Brain ImagesCode1
Analysis of Social Robotic Navigation approaches: CNN Encoder and Incremental Learning as an alternative to Deep Reinforcement Learning—0
Towards Closing the Sim-to-Real Gap in Collaborative Multi-Robot Deep Reinforcement LearningCode0
Super-Human Performance in Gran Turismo Sport Using Deep Reinforcement Learning—0
Residual Learning from Demonstration: Adapting DMPs for Contact-rich Manipulation—0
Reinforcement Learning for Improving Object Detection—0
ReLMoGen: Leveraging Motion Generation in Reinforcement Learning for Mobile Manipulation—0
A Survey on Reinforcement Learning for Combinatorial Optimization—0
SuperSuit: Simple Microwrappers for Reinforcement Learning EnvironmentsCode1
Playing Catan with Cross-dimensional Neural Network—0
DeepSlicing: Deep Reinforcement Learning Assisted Resource Allocation for Network Slicing—0
Generative Design by Reinforcement Learning: Enhancing the Diversity of Topology Optimization Designs—0
Forward and inverse reinforcement learning sharing network weights and hyperparameters—0
Model-Reference Reinforcement Learning for Collision-Free Tracking Control of Autonomous Surface Vehicles—0
On the Sample Complexity of Reinforcement Learning with Policy Space Generalization—0
DRL-Based QoS-Aware Resource Allocation Scheme for Coexistence of Licensed and Unlicensed Users in LTE and Beyond—0
Inverse Reinforcement Learning with Natural Language Goals—0
An adaptive synchronization approach for weights of deep reinforcement learning—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1PPGMean Normalized Performance0.76—Unverified
2PPOMean Normalized Performance0.58—Unverified