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 2601–2650 of 15113 papers

TitleStatusHype
General Method for Solving Four Types of SAT Problems—0
A Bayesian Framework of Deep Reinforcement Learning for Joint O-RAN/MEC Orchestration—0
LLMLight: Large Language Models as Traffic Signal Control AgentsCode2
Learning Online Policies for Person Tracking in Multi-View Environments—0
Efficient Reinforcement Learning via Decoupling Exploration and UtilizationCode1
PDiT: Interleaving Perception and Decision-making Transformers for Deep Reinforcement LearningCode1
Agent based modelling for continuously varying supply chains—0
Reinforcement Learning for Safe Occupancy Strategies in Educational Spaces during an Epidemic—0
Mutual Information as Intrinsic Reward of Reinforcement Learning Agents for On-demand Ride Pooling—0
Gradient Shaping for Multi-Constraint Safe Reinforcement Learning—0
Human-AI Collaboration in Real-World Complex Environment with Reinforcement Learning—0
Hardware-Aware DNN Compression via Diverse Pruning and Mixed-Precision Quantization—0
REBEL: Reward Regularization-Based Approach for Robotic Reinforcement Learning from Human Feedback—0
Pangu-Agent: A Fine-Tunable Generalist Agent with Structured Reasoning—0
A Survey of Reinforcement Learning from Human Feedback—0
Multiagent Copilot Approach for Shared Autonomy between Human EEG and TD3 Deep Reinforcement Learning—0
Critic-Guided Decision Transformer for Offline Reinforcement LearningCode1
Multi-Agent Probabilistic Ensembles with Trajectory Sampling for Connected Autonomous Vehicles—0
Maximum entropy GFlowNets with soft Q-learning—0
Optimizing Heat Alert Issuance with Reinforcement LearningCode0
Diffusion Reward: Learning Rewards via Conditional Video DiffusionCode1
RFRL Gym: A Reinforcement Learning Testbed for Cognitive Radio ApplicationsCode1
OpenRL: A Unified Reinforcement Learning FrameworkCode2
Optimal coordination of resources: A solution from reinforcement learning—0
Towards Machines that Trust: AI Agents Learn to Trust in the Trust Game—0
Parameterized Projected Bellman OperatorCode0
BadRL: Sparse Targeted Backdoor Attack Against Reinforcement LearningCode0
Data-Driven Merton's Strategies via Policy Randomization—0
Stable Relay Learning Optimization Approach for Fast Power System Production Cost Minimization Simulation—0
CUDC: A Curiosity-Driven Unsupervised Data Collection Method with Adaptive Temporal Distances for Offline Reinforcement Learning—0
A Dual Curriculum Learning Framework for Multi-UAV Pursuit-Evasion in Diverse Environments—0
Neural Network Approximation for Pessimistic Offline Reinforcement Learning—0
Solving the swing-up and balance task for the Acrobot and Pendubot with SAC—0
Safeguarded Progress in Reinforcement Learning: Safe Bayesian Exploration for Control Policy Synthesis—0
Active search and coverage using point-cloud reinforcement learning—0
Challenges for Reinforcement Learning in Quantum Circuit DesignCode1
Learning to Act without ActionsCode1
CACTO-SL: Using Sobolev Learning to improve Continuous Actor-Critic with Trajectory OptimizationCode1
Improving Environment Robustness of Deep Reinforcement Learning Approaches for Autonomous Racing Using Bayesian Optimization-based Curriculum LearningCode0
Active Reinforcement Learning for Robust Building ControlCode1
Imitate the Good and Avoid the Bad: An Incremental Approach to Safe Reinforcement LearningCode0
Advancing RAN Slicing with Offline Reinforcement Learning—0
Online Restless Multi-Armed Bandits with Long-Term Fairness Constraints—0
Fractional Deep Reinforcement Learning for Age-Minimal Mobile Edge Computing—0
Assume-Guarantee Reinforcement Learning—0
Toward Computationally Efficient Inverse Reinforcement Learning via Reward Shaping—0
Towards Automatic Data Augmentation for Disordered Speech Recognition—0
iOn-Profiler: intelligent Online multi-objective VNF Profiling with Reinforcement Learning—0
ReCoRe: Regularized Contrastive Representation Learning of World Model—0
World Models via Policy-Guided Trajectory DiffusionCode1
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Benchmark Results

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