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 4051–4075 of 15113 papers

TitleStatusHype
Stabilizing Visual Reinforcement Learning via Asymmetric Interactive Cooperation—0
PolicyCleanse: Backdoor Detection and Mitigation for Competitive Reinforcement Learning—0
Simoun: Synergizing Interactive Motion-appearance Understanding for Vision-based Reinforcement Learning—0
Co-Speech Gesture Synthesis by Reinforcement Learning With Contrastive Pre-Trained RewardsCode0
Local-Guided Global: Paired Similarity Representation for Visual Reinforcement Learning—0
Second Thoughts are Best: Learning to Re-Align With Human Values from Text Edits—0
Optimization of Image Transmission in a Cooperative Semantic Communication Networks—0
Goal-Guided Transformer-Enabled Reinforcement Learning for Efficient Autonomous NavigationCode1
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
New Challenges in Reinforcement Learning: A Survey of Security and Privacy—0
Self-Activating Neural Ensembles for Continual Reinforcement LearningCode1
Risk-Sensitive Policy with Distributional Reinforcement LearningCode1
Transformer in Transformer as Backbone for Deep Reinforcement LearningCode1
Reinforcement Learning with Success Induced Task PrioritizationCode0
Pontryagin Optimal Control via Neural NetworksCode0
POMRL: No-Regret Learning-to-Plan with Increasing Horizons—0
Symbolic Visual Reinforcement Learning: A Scalable Framework with Object-Level Abstraction and Differentiable Expression SearchCode1
RL and Fingerprinting to Select Moving Target Defense Mechanisms for Zero-day Attacks in IoTCode0
Hybrid Deep Reinforcement Learning and Planning for Safe and Comfortable Automated Driving—0
Backward Curriculum Reinforcement Learning—0
Federated Multi-Agent Deep Reinforcement Learning Approach via Physics-Informed Reward for Multi-Microgrid Energy Management—0
A Novel Experts Advice Aggregation Framework Using Deep Reinforcement Learning for Portfolio Management—0
On the Geometry of Reinforcement Learning in Continuous State and Action Spaces—0
On Transforming Reinforcement Learning by Transformer: The Development Trajectory—0
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Benchmark Results

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