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 5701–5725 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
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Benchmark Results

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