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 7126–7150 of 15113 papers

TitleStatusHype
Value Summation: A Novel Scoring Function for MPC-based Model-based Reinforcement Learning—0
VARD: Efficient and Dense Fine-Tuning for Diffusion Models with Value-based RL—0
Variable Compliance Control for Robotic Peg-in-Hole Assembly: A Deep Reinforcement Learning Approach—0
Variable Gain Gradient Descent-based Reinforcement Learning for Robust Optimal Tracking Control of Uncertain Nonlinear System with Input-Constraints—0
Variable Impedance Control in End-Effector Space: An Action Space for Reinforcement Learning in Contact-Rich Tasks—0
Variance-Aware Off-Policy Evaluation with Linear Function Approximation—0
Variance-Aware Regret Bounds for Undiscounted Reinforcement Learning in MDPs—0
Variance-aware robust reinforcement learning with linear function approximation under heavy-tailed rewards—0
Variance-Based Risk Estimations in Markov Processes via Transformation with State Lumping—0
Variance Reduced Advantage Estimation with δ Hindsight Credit Assignment—0
Variance-Reduced Conservative Policy Iteration—0
Variance-Reduced Off-Policy Memory-Efficient Policy Search—0
Variance Reduction for Deep Q-Learning using Stochastic Recursive Gradient—0
Variance Reduction for Evolution Strategies via Structured Control Variates—0
Variance Reduction for Policy-Gradient Methods via Empirical Variance Minimization—0
Variance Reduction for Policy Gradient with Action-Dependent Factorized Baselines—0
Variance Reduction for Reinforcement Learning in Input-Driven Environments—0
Variance Reduction Methods for Sublinear Reinforcement Learning—0
Variational Adaptive-Newton Method for Explorative Learning—0
Variational Bayes: A report on approaches and applications—0
Variational Bayesian Reinforcement Learning with Regret Bounds—0
Variational Constrained Reinforcement Learning with Application to Planning at Roundabout—0
Variational Dynamic for Self-Supervised Exploration in Deep Reinforcement Learning—0
Variational Empowerment as Representation Learning for Goal-Based Reinforcement Learning—0
Variational Inference for Model-Free and Model-Based Reinforcement Learning—0
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Benchmark Results

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