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 11576–11600 of 15113 papers

TitleStatusHype
Improving Assistive Robotics with Deep Reinforcement Learning—0
Improving Context-Based Meta-Reinforcement Learning with Self-Supervised Trajectory Contrastive Learning—0
Improving Cost Learning for JPEG Steganography by Exploiting JPEG Domain Knowledge—0
Improving Deep Reinforcement Learning in Minecraft with Action Advice—0
Improving Document Image Understanding with Reinforcement Finetuning—0
Improving Exploration of Deep Reinforcement Learning using Planning for Policy Search—0
Improving Fictitious Play Reinforcement Learning with Expanding Models—0
Improving gearshift controllers for electric vehicles with reinforcement learning—0
Improving Generalization in Intent Detection: GRPO with Reward-Based Curriculum Sampling—0
Improving Generalization in Meta Reinforcement Learning using Learned Objectives—0
Improving generalization in reinforcement learning through forked agents—0
Improving Generalization of Alignment with Human Preferences through Group Invariant Learning—0
Improving Generalization of Deep Reinforcement Learning-based TSP Solvers—0
Improving Generalization of Reinforcement Learning with Minimax Distributional Soft Actor-Critic—0
Improving Gradient Estimation in Evolutionary Strategies With Past Descent Directions—0
Improving Human Decision-Making by Discovering Efficient Strategies for Hierarchical Planning—0
Improving Hyperparameter Optimization by Planning Ahead—0
Improving Input-Output Linearizing Controllers for Bipedal Robots via Reinforcement Learning—0
Improving Interaction Quality Estimation with BiLSTMs and the Impact on Dialogue Policy Learning—0
Improving Interactive Reinforcement Agent Planning with Human Demonstration—0
Improving interactive reinforcement learning: What makes a good teacher?—0
Improving Intrinsic Exploration with Language Abstractions—0
Improving Learning from Demonstrations by Learning from Experience—0
Improving Learning to Branch via Reinforcement Learning—0
Improving Long-Term Metrics in Recommendation Systems using Short-Horizon Reinforcement Learning—0
Show:102550
← PrevPage 464 of 605Next →

Benchmark Results

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