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 3326–3350 of 15113 papers

TitleStatusHype
Multi-hop Reading Comprehension via Deep Reinforcement Learning based Document TraversalCode0
A Reinforcement Learning Approach to Domain-Knowledge Inclusion Using Grammar Guided Symbolic RegressionCode0
Computational Benefits of Intermediate Rewards for Goal-Reaching Policy LearningCode0
Hard Prompts Made Interpretable: Sparse Entropy Regularization for Prompt Tuning with RLCode0
HAMMER: Multi-Level Coordination of Reinforcement Learning Agents via Learned MessagingCode0
Handling Delay in Real-Time Reinforcement LearningCode0
Multiple Object Recognition with Visual AttentionCode0
gym-gazebo2, a toolkit for reinforcement learning using ROS 2 and GazeboCode0
Decision-Aware Actor-Critic with Function Approximation and Theoretical GuaranteesCode0
Gym-Ignition: Reproducible Robotic Simulations for Reinforcement LearningCode0
GuideLight: "Industrial Solution" Guidance for More Practical Traffic Signal Control AgentsCode0
Multi-task Deep Reinforcement Learning with PopArtCode0
Adversarial Online Multi-Task Reinforcement LearningCode0
Guiding Evolutionary Strategies by Differentiable Robot SimulatorsCode0
A Reinforcement Learning Approach to Interactive-Predictive Neural Machine TranslationCode0
Active Object Localization with Deep Reinforcement LearningCode0
Guided Policy Optimization under Partial ObservabilityCode0
Guided Dialog Policy Learning: Reward Estimation for Multi-Domain Task-Oriented DialogCode0
Guided Deep Reinforcement Learning for Swarm SystemsCode0
Guided Dialog Policy Learning without Adversarial Learning in the LoopCode0
Multivariate Time Series Early Classification Across Channel and Time DimensionsCode0
Compositional Learning of Visually-Grounded Concepts Using ReinforcementCode0
Guide Actor-Critic for Continuous ControlCode0
Guided Cooperation in Hierarchical Reinforcement Learning via Model-based RolloutCode0
Guided Dialogue Policy Learning without Adversarial Learning in the LoopCode0
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Benchmark Results

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