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 1751–1775 of 15113 papers

TitleStatusHype
Knowledge Transfer in Multi-Task Deep Reinforcement Learning for Continuous ControlCode1
Masked Contrastive Representation Learning for Reinforcement LearningCode1
A game-theoretic analysis of networked system control for common-pool resource management using multi-agent reinforcement learningCode1
Constrained Model-based Reinforcement Learning with Robust Cross-Entropy MethodCode1
UAV Path Planning using Global and Local Map Information with Deep Reinforcement LearningCode1
Modeling Protagonist Emotions for Emotion-Aware StorytellingCode1
Measuring Visual Generalization in Continuous Control from PixelsCode1
Deep Reinforcement Learning for Real-Time Optimization of Pumps in Water Distribution SystemsCode1
Efficient Wasserstein Natural Gradients for Reinforcement LearningCode1
Distributed Resource Allocation with Multi-Agent Deep Reinforcement Learning for 5G-V2V CommunicationCode1
LaND: Learning to Navigate from DisengagementsCode1
Graph Convolutional Value Decomposition in Multi-Agent Reinforcement LearningCode1
EpidemiOptim: A Toolbox for the Optimization of Control Policies in Epidemiological ModelsCode1
Trajectory Inspection: A Method for Iterative Clinician-Driven Design of Reinforcement Learning StudiesCode1
Text-based RL Agents with Commonsense Knowledge: New Challenges, Environments and BaselinesCode1
CausalWorld: A Robotic Manipulation Benchmark for Causal Structure and Transfer LearningCode1
Neural Mask Generator: Learning to Generate Adaptive Word Maskings for Language Model AdaptationCode1
Reward Machines: Exploiting Reward Function Structure in Reinforcement LearningCode1
Reinforcement Learning with Random DelaysCode1
Interactive Fiction Game Playing as Multi-Paragraph Reading Comprehension with Reinforcement LearningCode1
A Reinforcement Learning Approach for Rebalancing Electric Vehicle Sharing SystemsCode1
Action Guidance: Getting the Best of Sparse Rewards and Shaped Rewards for Real-time Strategy GamesCode1
FORK: A Forward-Looking Actor For Model-Free Reinforcement LearningCode1
Goal-Auxiliary Actor-Critic for 6D Robotic Grasping with Point CloudsCode1
Self-Play Reinforcement Learning for Fast Image RetargetingCode1
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Benchmark Results

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