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 2601–2625 of 15113 papers

TitleStatusHype
General Method for Solving Four Types of SAT Problems—0
Learning Online Policies for Person Tracking in Multi-View Environments—0
A Bayesian Framework of Deep Reinforcement Learning for Joint O-RAN/MEC Orchestration—0
Efficient Reinforcement Learning via Decoupling Exploration and UtilizationCode1
PDiT: Interleaving Perception and Decision-making Transformers for Deep Reinforcement LearningCode1
LLMLight: Large Language Models as Traffic Signal Control AgentsCode2
Agent based modelling for continuously varying supply chains—0
Human-AI Collaboration in Real-World Complex Environment with Reinforcement Learning—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
Hardware-Aware DNN Compression via Diverse Pruning and Mixed-Precision Quantization—0
Reinforcement Learning for Safe Occupancy Strategies in Educational Spaces during an Epidemic—0
REBEL: Reward Regularization-Based Approach for Robotic Reinforcement Learning from Human Feedback—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
Pangu-Agent: A Fine-Tunable Generalist Agent with Structured Reasoning—0
Diffusion Reward: Learning Rewards via Conditional Video DiffusionCode1
Critic-Guided Decision Transformer for Offline Reinforcement LearningCode1
Maximum entropy GFlowNets with soft Q-learning—0
Optimizing Heat Alert Issuance with Reinforcement LearningCode0
Multi-Agent Probabilistic Ensembles with Trajectory Sampling for Connected Autonomous Vehicles—0
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
Parameterized Projected Bellman OperatorCode0
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Benchmark Results

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