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 7351–7375 of 15113 papers

TitleStatusHype
PEARL: Zero-shot Cross-task Preference Alignment and Robust Reward Learning for Robotic Manipulation—0
Zero-Shot Reinforcement Learning on Graphs for Autonomous Exploration Under Uncertainty—0
Zero-Shot Reinforcement Learning with Deep Attention Convolutional Neural Networks—0
Zero-Shot Reward Specification via Grounded Natural Language—0
Sim-to-Real Transfer of Robot Learning with Variable Length Inputs—0
Zero-shot Text Classification via Reinforced Self-training—0
Zero-Shot Transfer with Deictic Object-Oriented Representation in Reinforcement Learning—0
Zero-Shot Uncertainty-Aware Deployment of Simulation Trained Policies on Real-World Robots—0
Zero-Sum Positional Differential Games as a Framework for Robust Reinforcement Learning: Deep Q-Learning Approach—0
Zeroth-order Informed Fine-Tuning for Diffusion Model: A Recursive Likelihood Ratio Optimizer—0
Zeroth-Order Optimization is Secretly Single-Step Policy Optimization—0
Zeroth-Order Supervised Policy Improvement—0
Zeus: Efficiently Localizing Actions in Videos using Reinforcement Learning—0
Zooming for Efficient Model-Free Reinforcement Learning in Metric Spaces—0
Reinforcement Learning in Low-Rank MDPs with Density Features—0
Low-Resource Machine Translation based on Asynchronous Dynamic Programming—0
Low-Switching Policy Gradient with Exploration via Online Sensitivity Sampling—0
Low-Thrust Orbital Transfer using Dynamics-Agnostic Reinforcement Learning—0
LPaintB: Learning to Paint from Self-Supervision—0
LPMARL: Linear Programming based Implicit Task Assigment for Hiearchical Multi-Agent Reinforcement Learning—0
LSD-Net: Look, Step and Detect for Joint Navigation and Multi-View Recognition with Deep Reinforcement Learning—0
LSTD with Random Projections—0
LUCIFER: Language Understanding and Context-Infused Framework for Exploration and Behavior Refinement—0
Lyapunov-Based Reinforcement Learning for Decentralized Multi-Agent Control—0
Lyapunov-Based Reinforcement Learning State Estimator—0
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Benchmark Results

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