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 5551–5600 of 15113 papers

TitleStatusHype
Scalable Communication for Multi-Agent Reinforcement Learning via Transformer-Based Email Mechanism—0
UAV aided Metaverse over Wireless Communications: A Reinforcement Learning Approach—0
Robofriend: An Adpative Storytelling Robotic Teddy Bear - Technical ReportCode0
Learning-based MPC from Big Data Using Reinforcement Learning—0
Contextual Conservative Q-Learning for Offline Reinforcement Learning—0
A Succinct Summary of Reinforcement Learning—0
Towards Deployable RL - What's Broken with RL Research and a Potential Fix—0
Safe Reinforcement Learning for an Energy-Efficient Driver Assistance System—0
Offline Evaluation for Reinforcement Learning-based Recommendation: A Critical Issue and Some Alternatives—0
Temporal Difference Learning with Compressed Updates: Error-Feedback meets Reinforcement Learning—0
Safety Filtering for Reinforcement Learning-based Adaptive Cruise Control—0
On the Challenges of using Reinforcement Learning in Precision Drug Dosing: Delay and Prolongedness of Action EffectsCode0
Large-Scale Traffic Signal Control by a Nash Deep Q-network Approach—0
Deep Reinforcement Learning for Asset Allocation: Reward Clipping—0
Deep reinforcement learning for irrigation scheduling using high-dimensional sensor feedbackCode0
A Policy Optimization Method Towards Optimal-time Stability—0
Co-Speech Gesture Synthesis by Reinforcement Learning With Contrastive Pre-Trained RewardsCode0
Local-Guided Global: Paired Similarity Representation for Visual Reinforcement Learning—0
Optimization of Image Transmission in a Cooperative Semantic Communication Networks—0
Second Thoughts are Best: Learning to Re-Align With Human Values from Text Edits—0
Stabilizing Visual Reinforcement Learning via Asymmetric Interactive Cooperation—0
Simoun: Synergizing Interactive Motion-appearance Understanding for Vision-based Reinforcement Learning—0
PolicyCleanse: Backdoor Detection and Mitigation for Competitive Reinforcement Learning—0
New Challenges in Reinforcement Learning: A Survey of Security and Privacy—0
Cost-Effective Two-Stage Network Slicing for Edge-Cloud Orchestrated Vehicular Networks—0
Accuracy-Guaranteed Collaborative DNN Inference in Industrial IoT via Deep Reinforcement Learning—0
Hybrid Deep Reinforcement Learning and Planning for Safe and Comfortable Automated Driving—0
POMRL: No-Regret Learning-to-Plan with Increasing Horizons—0
Pontryagin Optimal Control via Neural NetworksCode0
Reinforcement Learning with Success Induced Task PrioritizationCode0
RL and Fingerprinting to Select Moving Target Defense Mechanisms for Zero-day Attacks in IoTCode0
Offline Policy Optimization in RL with Variance Regularizaton—0
On Transforming Reinforcement Learning by Transformer: The Development Trajectory—0
On the Geometry of Reinforcement Learning in Continuous State and Action Spaces—0
Federated Multi-Agent Deep Reinforcement Learning Approach via Physics-Informed Reward for Multi-Microgrid Energy Management—0
Backward Curriculum Reinforcement Learning—0
A Novel Experts Advice Aggregation Framework Using Deep Reinforcement Learning for Portfolio Management—0
Certifying Safety in Reinforcement Learning under Adversarial Perturbation Attacks—0
Don't do it: Safer Reinforcement Learning With Rule-based Guidance—0
Improving a sequence-to-sequence nlp model using a reinforcement learning policy algorithm—0
Representation Learning in Deep RL via Discrete Information Bottleneck—0
Towards automating Codenames spymasters with deep reinforcement learning—0
Offline Reinforcement Learning via Linear-Programming with Error-Bound Induced Constraints—0
Towards Learning Abstractions via Reinforcement Learning—0
On the Convergence of Discounted Policy Gradient Methods—0
Optimal scheduling of island integrated energy systems considering multi-uncertainties and hydrothermal simultaneous transmission: A deep reinforcement learning approach—0
Model-Based Reinforcement Learning with Multinomial Logistic Function Approximation—0
Strangeness-driven Exploration in Multi-Agent Reinforcement LearningCode0
Data-driven control of COVID-19 in buildings: a reinforcement-learning approach—0
Learning Generalizable Representations for Reinforcement Learning via Adaptive Meta-learner of Behavioral SimilaritiesCode0
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Benchmark Results

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