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 6401–6425 of 15113 papers

TitleStatusHype
Diagnosing Reinforcement Learning for Traffic Signal Control—0
Dialog Action-Aware Transformer for Dialog Policy Learning—0
Dialogue Evaluation with Offline Reinforcement Learning—0
Dialogue manager domain adaptation using Gaussian process reinforcement learning—0
Dialogue Shaping: Empowering Agents through NPC Interaction—0
DiBB: Distributing Black-Box Optimization—0
Dichotomy of Control: Separating What You Can Control from What You Cannot—0
Diff-DAC: Distributed Actor-Critic for Average Multitask Deep Reinforcement Learning—0
Difference of Convex Functions Programming Applied to Control with Expert Data—0
Difference of Convex Functions Programming for Reinforcement Learning—0
Difference Rewards Policy Gradients—0
Differentiable Arbitrating in Zero-sum Markov Games—0
Differentiable Discrete Event Simulation for Queuing Network Control—0
Differentiable Logic Machines—0
Differentiable Physics Models for Real-world Offline Model-based Reinforcement Learning—0
Differentiable Quantum Architecture Search in Asynchronous Quantum Reinforcement Learning—0
Differentially Private Exploration in Reinforcement Learning with Linear Representation—0
Differentially Private Deep Model-Based Reinforcement Learning—0
Differentially Private Policy Evaluation—0
Differentially Private Reinforcement Learning with Linear Function Approximation—0
Differential Variable Speed Limits Control for Freeway Recurrent Bottlenecks via Deep Reinforcement learning—0
Differentiated Federated Reinforcement Learning Based Traffic Offloading on Space-Air-Ground Integrated Networks—0
DiffPoGAN: Diffusion Policies with Generative Adversarial Networks for Offline Reinforcement Learning—0
DiffSkill: Skill Abstraction from Differentiable Physics for Deformable Object Manipulations with Tools—0
DiffStitch: Boosting Offline Reinforcement Learning with Diffusion-based Trajectory Stitching—0
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Benchmark Results

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