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 5676–5700 of 15113 papers

TitleStatusHype
Joint Attention for Multi-Agent Coordination and Social Learning—0
Joint Band Assignment and Beam Management using Hierarchical Reinforcement Learning for Multi-Band Communication—0
Learning Reward and Policy Jointly from Demonstration and Preference Improves Alignment—0
Joint Differentiable Optimization and Verification for Certified Reinforcement Learning—0
Joint Energy Dispatch and Unit Commitment in Microgrids Based on Deep Reinforcement Learning—0
Joint Entity Linking with Deep Reinforcement Learning—0
Joint Goal and Strategy Inference across Heterogeneous Demonstrators via Reward Network Distillation—0
Joint Inference of Reward Machines and Policies for Reinforcement Learning—0
Joint Learning-Based Stabilization of Multiple Unknown Linear Systems—0
Joint Learning of Policy with Unknown Temporal Constraints for Safe Reinforcement Learning—0
Joint Learning of Reward Machines and Policies in Environments with Partially Known Semantics—0
Jointly Reinforced User Simulator and Task-oriented Dialog System with Simplified Generative Architecture—0
Jointly-Trained State-Action Embedding for Efficient Reinforcement Learning—0
Jointly Training and Pruning CNNs via Learnable Agent Guidance and Alignment—0
Joint Modeling for Learning Decision-Making Dynamics in Behavioral Experiments—0
Joint Modeling for Query Expansion and Information Extraction with Reinforcement Learning—0
Joint Optimization of Multi-Objective Reinforcement Learning with Policy Gradient Based Algorithm—0
Joint Power Allocation and Beamformer for mmW-NOMA Downlink Systems by Deep Reinforcement Learning—0
Joint Representation Training in Sequential Tasks with Shared Structure—0
Joint Resource Management for MC-NOMA: A Deep Reinforcement Learning Approach—0
Joint Self-Supervised Learning for Vision-based Reinforcement Learning—0
Joint Sensing and Communications for Deep Reinforcement Learning-based Beam Management in 6G—0
Jointly-Learned State-Action Embedding for Efficient Reinforcement Learning—0
Joint Synthesis of Safety Certificate and Safe Control Policy using Constrained Reinforcement Learning—0
JudgeLRM: Large Reasoning Models as a Judge—0
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Benchmark Results

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