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

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Showing 1176–1200 of 15113 papers

TitleStatusHype
Curiosity-Driven Energy-Efficient Worker Scheduling in Vehicular Crowdsourcing: A Deep Reinforcement Learning ApproachCode1
An Optimistic Perspective on Offline Deep Reinforcement LearningCode1
Curious Hierarchical Actor-Critic Reinforcement LearningCode1
An Inductive Bias for Distances: Neural Nets that Respect the Triangle InequalityCode1
Curriculum-based Asymmetric Multi-task Reinforcement LearningCode1
Curriculum Offline Imitation LearningCode1
Curriculum Reinforcement Learning using Optimal Transport via Gradual Domain AdaptationCode1
Data-Efficient Reinforcement Learning with Self-Predictive RepresentationsCode1
DataLight: Offline Data-Driven Traffic Signal ControlCode1
A Policy-Guided Imitation Approach for Offline Reinforcement LearningCode1
Compositional Reinforcement Learning from Logical SpecificationsCode1
Debiasing Meta-Gradient Reinforcement Learning by Learning the Outer Value FunctionCode1
Decentralized Deep Reinforcement Learning for a Distributed and Adaptive Locomotion Controller of a Hexapod RobotCode1
Decentralized Structural-RNN for Robot Crowd Navigation with Deep Reinforcement LearningCode1
Deceptive Path Planning via Reinforcement Learning with Graph Neural NetworksCode1
Concise Reasoning via Reinforcement LearningCode1
Decoupling Strategy and Generation in Negotiation DialoguesCode1
Deep Active Inference for Partially Observable MDPsCode1
Deep Actor-Critic Learning for Distributed Power Control in Wireless Mobile NetworksCode1
Comparing Popular Simulation Environments in the Scope of Robotics and Reinforcement LearningCode1
Deep Implicit Coordination Graphs for Multi-agent Reinforcement LearningCode1
A deep inverse reinforcement learning approach to route choice modeling with context-dependent rewardsCode1
Deep Laplacian-based Options for Temporally-Extended ExplorationCode1
Approximate information state for approximate planning and reinforcement learning in partially observed systemsCode1
Comparing Observation and Action Representations for Deep Reinforcement Learning in μRTSCode1
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Benchmark Results

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