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 4551–4575 of 15113 papers

TitleStatusHype
Hypernetworks in Meta-Reinforcement LearningCode1
Robotic Table Wiping via Reinforcement Learning and Whole-body Trajectory Optimization—0
Oracles & Followers: Stackelberg Equilibria in Deep Multi-Agent Reinforcement Learning—0
On the Power of Pre-training for Generalization in RL: Provable Benefits and Hardness—0
On the Feasibility of Cross-Task Transfer with Model-Based Reinforcement LearningCode1
Palm up: Playing in the Latent Manifold for Unsupervised Pretraining—0
Provably Safe Reinforcement Learning via Action Projection using Reachability Analysis and Polynomial Zonotopes—0
Robot Navigation with Reinforcement Learned Path Generation and Fine-Tuned Motion Control—0
Robust Offline Reinforcement Learning with Gradient Penalty and Constraint Relaxation—0
Scaling Laws for Reward Model Overoptimization—0
When to Ask for Help: Proactive Interventions in Autonomous Reinforcement LearningCode0
Hierarchical Reinforcement Learning for Furniture Layout in Virtual Indoor Scenes—0
A Reinforcement Learning Approach in Multi-Phase Second-Price Auction Design—0
Learning Preferences for Interactive AutonomyCode0
CLUTR: Curriculum Learning via Unsupervised Task Representation LearningCode0
Integrated Decision and Control for High-Level Automated Vehicles by Mixed Policy Gradient and Its Experiment Verification—0
DIAMBRA Arena: a New Reinforcement Learning Platform for Research and ExperimentationCode2
Curriculum Reinforcement Learning using Optimal Transport via Gradual Domain AdaptationCode1
Deep Black-Box Reinforcement Learning with Movement PrimitivesCode1
CEIP: Combining Explicit and Implicit Priors for Reinforcement Learning with DemonstrationsCode0
Rethinking Value Function Learning for Generalization in Reinforcement LearningCode1
RPM: Generalizable Behaviors for Multi-Agent Reinforcement Learning—0
Unpacking Reward Shaping: Understanding the Benefits of Reward Engineering on Sample Complexity—0
Model Predictive Control via On-Policy Imitation Learning—0
On Uncertainty in Deep State Space Models for Model-Based Reinforcement LearningCode1
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Benchmark Results

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