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 4951–5000 of 15113 papers

TitleStatusHype
Intrinsic fluctuations of reinforcement learning promote cooperationCode0
A Technique to Create Weaker Abstract Board Game Agents via Reinforcement Learning—0
Dynamics-Adaptive Continual Reinforcement Learning via Progressive Contextualization—0
Deep reinforcement learning for quantum multiparameter estimation—0
Transmit Power Control for Indoor Small Cells: A Method Based on Federated Reinforcement Learning—0
Rethinking Conversational Recommendations: Is Decision Tree All You Need?Code1
A stabilizing reinforcement learning approach for sampled systems with partially unknown models—0
Deep Anomaly Detection and Search via Reinforcement Learning—0
Cell-Free Latent Go-ExploreCode1
Style-Agnostic Reinforcement LearningCode1
Model-Based Reinforcement Learning with SINDy—0
A further exploration of deep Multi-Agent Reinforcement Learning with Hybrid Action Space—0
An Analysis of Model-Based Reinforcement Learning From Abstracted Observations—0
Beyond Supervised Continual Learning: a Review—0
Distributed Ensembles of Reinforcement Learning Agents for Electricity Control—0
Effective Multi-User Delay-Constrained Scheduling with Deep Recurrent Reinforcement LearningCode1
Evolutionary Deep Reinforcement Learning for Dynamic Slice Management in O-RAN—0
Digital Twin Assisted Risk-Aware Sleep Mode Management Using Deep Q-Networks—0
Reinforcement Learning for Hardware Security: Opportunities, Developments, and Challenges—0
Understanding the Limits of Poisoning Attacks in Episodic Reinforcement Learning—0
Categorical semantics of compositional reinforcement learning—0
Goal-Conditioned Q-Learning as Knowledge DistillationCode0
Normality-Guided Distributional Reinforcement Learning for Continuous Control—0
Unsupervised Representation Learning in Deep Reinforcement Learning: A ReviewCode0
SupervisorBot: NLP-Annotated Real-Time Recommendations of Psychotherapy Treatment Strategies with Deep Reinforcement Learning—0
RL-DistPrivacy: Privacy-Aware Distributed Deep Inference for low latency IoT systems—0
CH-MARL: A Multimodal Benchmark for Cooperative, Heterogeneous Multi-Agent Reinforcement Learning—0
DETERRENT: Detecting Trojans using Reinforcement Learning—0
ATTRITION: Attacking Static Hardware Trojan Detection Techniques Using Reinforcement Learning—0
An approach to implement Reinforcement Learning for Heterogeneous Vehicular Networks—0
Exploiting Deep Reinforcement Learning for Edge Caching in Cell-Free Massive MIMO Systems—0
Reinforcement Learning based Multi-connectivity Resource Allocation in Factory Automation Systems—0
Socially Fair Reinforcement Learning—0
Visual processing in context of reinforcement learning—0
Symbolic Explanation of Affinity-Based Reinforcement Learning Agents with Markov Models—0
Play with Emotion: Affect-Driven Reinforcement Learning—0
Towards Automated Imbalanced Learning with Deep Hierarchical Reinforcement LearningCode1
Importance Prioritized Policy DistillationCode0
Light-weight probing of unsupervised representations for Reinforcement LearningCode1
A Comparison of Reinforcement Learning Frameworks for Software Testing TasksCode0
Autonomous Unmanned Aerial Vehicle Navigation using Reinforcement Learning: A Systematic Review—0
Learning Task Automata for Reinforcement Learning using Hidden Markov Models—0
Turning Mathematics Problems into Games: Reinforcement Learning and Gröbner bases together solve Integer Feasibility Problems—0
UAS Navigation in the Real World Using Visual Observation—0
Variance Reduction based Experience Replay for Policy OptimizationCode0
Oracle-free Reinforcement Learning in Mean-Field Games along a Single Sample Path—0
Augmenting Reinforcement Learning with Transformer-based Scene Representation Learning for Decision-making of Autonomous DrivingCode1
Hierarchical Reinforcement Learning Based Video Semantic Coding for Segmentation—0
Dynamic Memory-based Curiosity: A Bootstrap Approach for Exploration—0
A model-based approach to meta-Reinforcement Learning: Transformers and tree search—0
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Benchmark Results

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