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 5701–5750 of 15113 papers

TitleStatusHype
JueWu-MC: Playing Minecraft with Sample-efficient Hierarchical Reinforcement Learning—0
JUNO: Jump-Start Reinforcement Learning-based Node Selection for UWB Indoor Localization—0
Just Ask:An Interactive Learning Framework for Vision and Language Navigation—0
KalMamba: Towards Efficient Probabilistic State Space Models for RL under Uncertainty—0
Kalman Temporal Differences—0
KAN v.s. MLP for Offline Reinforcement Learning—0
KARL: Knowledge-Aware Reasoning Memory Modeling with Reinforcement Learning of Vector Space—0
KCRL: Krasovskii-Constrained Reinforcement Learning with Guaranteed Stability in Nonlinear Dynamical Systems—0
KDRL: Post-Training Reasoning LLMs via Unified Knowledge Distillation and Reinforcement Learning—0
Keep Doing What Worked: Behavioral Modelling Priors for Offline Reinforcement Learning—0
Keep Doing What Worked: Behavior Modelling Priors for Offline Reinforcement Learning—0
Kernel-Based Distributed Q-Learning: A Scalable Reinforcement Learning Approach for Dynamic Treatment Regimes—0
Kernelized Reinforcement Learning with Order Optimal Regret Bounds—0
Kevin: Multi-Turn RL for Generating CUDA Kernels—0
KF-LAX: Kronecker-factored curvature estimation for control variate optimization in reinforcement learning—0
KGRGRL: A User's Permission Reasoning Method Based on Knowledge Graph Reward Guidance Reinforcement Learning—0
Kick-motion Training with DQN in AI Soccer Environment—0
Kickstarting Deep Reinforcement Learning—0
Kinematics-Guided Reinforcement Learning for Object-Aware 3D Ego-Pose Estimation—0
Kinematic State Abstraction and Provably Efficient Rich-Observation Reinforcement Learning—0
KIPPO: Koopman-Inspired Proximal Policy Optimization—0
KL-Entropy-Regularized RL with a Generative Model is Minimax Optimal—0
k-Means Maximum Entropy Exploration—0
K-nearest Multi-agent Deep Reinforcement Learning for Collaborative Tasks with a Variable Number of Agents—0
KnowGPT: Knowledge Graph based Prompting for Large Language Models—0
Knowing the Past to Predict the Future: Reinforcement Virtual Learning—0
Knowledgeable Agents by Offline Reinforcement Learning from Large Language Model Rollouts—0
Knowledge accumulating: The general pattern of learning—0
Knowledge-Assisted Deep Reinforcement Learning in 5G Scheduler Design: From Theoretical Framework to Implementation—0
Knowledge-Based Sequential Decision-Making Under Uncertainty—0
Knowledge capture, adaptation and composition (KCAC): A framework for cross-task curriculum learning in robotic manipulation—0
Knowledge-enhanced Agents for Interactive Text Games—0
Knowledge Flow: Improve Upon Your Teachers—0
Knowledge-guided Deep Reinforcement Learning for Interactive Recommendation—0
Knowledge-Guided Exploration in Deep Reinforcement Learning—0
Knowledge-Informed Auto-Penetration Testing Based on Reinforcement Learning with Reward Machine—0
Knowledge or Reasoning? A Close Look at How LLMs Think Across Domains—0
Knowledge Transfer for Cross-Domain Reinforcement Learning: A Systematic Review—0
Knowledge Transfer in Deep Reinforcement Learning for Slice-Aware Mobility Robustness Optimization—0
KnowRU: Knowledge Reusing via Knowledge Distillation in Multi-agent Reinforcement Learning—0
KnowSR: Knowledge Sharing among Homogeneous Agents in Multi-agent Reinforcement Learning—0
Know Your Boundaries: The Necessity of Explicit Behavioral Cloning in Offline RL—0
KoGuN: Accelerating Deep Reinforcement Learning via Integrating Human Suboptimal Knowledge—0
Koopman-Assisted Reinforcement Learning—0
Koopman Q-learning: Offline Reinforcement Learning via Symmetries of Dynamics—0
K-spin Hamiltonian for quantum-resolvable Markov decision processes—0
L2C2: Locally Lipschitz Continuous Constraint towards Stable and Smooth Reinforcement Learning—0
L^2NAS: Learning to Optimize Neural Architectures via Continuous-Action Reinforcement Learning—0
Mathematical Models and Reinforcement Learning based Evolutionary Algorithm Framework for Satellite Scheduling Problem—0
Lagrangian Duality in Reinforcement Learning—0
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Benchmark Results

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