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

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Showing 6276–6300 of 15113 papers

TitleStatusHype
Learning Synthetic Environments and Reward Networks for Reinforcement LearningCode1
Adversarially Trained Actor Critic for Offline Reinforcement LearningCode1
ASHA: Assistive Teleoperation via Human-in-the-Loop Reinforcement Learning—0
Reinforcement learning for multi-item retrieval in the puzzle-based storage systemCode0
Transfer Reinforcement Learning for Differing Action Spaces via Q-Network RepresentationsCode1
Offline Reinforcement Learning for Mobile Notifications—0
Learning Interpretable, High-Performing Policies for Autonomous DrivingCode1
A Reinforcement Learning Framework for PQoS in a Teleoperated Driving Scenario—0
Meta-Reinforcement Learning with Self-Modifying Networks—0
Malleable Agents for Re-Configurable Robotic Manipulators—0
Versatile Offline Imitation from Observations and Examples via Regularized State-Occupancy MatchingCode1
Video Violence Recognition and Localization Using a Semi-Supervised Hard Attention Model—0
Model-Free Reinforcement Learning for Symbolic Automata-encoded Objectives—0
Financial Vision Based Reinforcement Learning Trading Strategy—0
Deep Reinforcement Learning Assisted Federated Learning Algorithm for Data Management of IIoT—0
Network Resource Allocation Strategy Based on Deep Reinforcement Learning—0
Resource Management and Security Scheme of ICPSs and IoT Based on VNE AlgorithmCode1
Reward is not enough: can we liberate AI from the reinforcement learning paradigm?—0
Security-Aware Virtual Network Embedding Algorithm based on Reinforcement Learning—0
AI-as-a-Service Toolkit for Human-Centered Intelligence in Autonomous Driving—0
How to Leverage Unlabeled Data in Offline Reinforcement Learning—0
Influence-Augmented Local Simulators: A Scalable Solution for Fast Deep RL in Large Networked Systems—0
Challenging Common Assumptions in Convex Reinforcement Learning—0
Dynamic Virtual Network Embedding Algorithm based on Graph Convolution Neural Network and Reinforcement Learning—0
Improved Regret for Differentially Private Exploration in Linear MDP—0
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Benchmark Results

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