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 3226–3250 of 15113 papers

TitleStatusHype
MICRO: Model-Based Offline Reinforcement Learning with a Conservative Bellman OperatorCode0
A Hierarchical Framework for Relation Extraction with Reinforcement LearningCode0
High-Throughput Distributed Reinforcement Learning via Adaptive Policy SynchronizationCode0
Conditionally Elicitable Dynamic Risk Measures for Deep Reinforcement LearningCode0
Hierarchical Reinforcement Learning with Advantage-Based Auxiliary RewardsCode0
Meta-learning Convolutional Neural Architectures for Multi-target Concrete Defect Classification with the COncrete DEfect BRidge IMage DatasetCode0
Conditional Computation in Neural Networks for faster modelsCode0
Hierarchical Reinforcement Learning with Optimal Level Synchronization based on a Deep Generative ModelCode0
Mining-Gym: A Configurable RL Benchmarking Environment for Truck Dispatch SchedulingCode0
Hierarchical Reinforcement Learning with the MAXQ Value Function DecompositionCode0
H_ Model-free Reinforcement Learning with Robust Stability GuaranteeCode0
HRL4IN: Hierarchical Reinforcement Learning for Interactive Navigation with Mobile ManipulatorsCode0
Concurrent Meta Reinforcement LearningCode0
Hierarchical Object Detection with Deep Reinforcement LearningCode0
Concurrent Credit Assignment for Data-efficient Reinforcement LearningCode0
Concrete DropoutCode0
Data-Efficient Hierarchical Reinforcement LearningCode0
Hierarchical Reinforcement Learning for Concurrent Discovery of Compound and Composable PoliciesCode0
Hierarchically Structured Task-Agnostic Continual LearningCode0
Data-Efficient Off-Policy Policy Evaluation for Reinforcement LearningCode0
A Systematization of the Wagner Framework: Graph Theory Conjectures and Reinforcement LearningCode0
Hierarchical Deep Reinforcement Learning: Integrating Temporal Abstraction and Intrinsic MotivationCode0
Hierarchical Meta Reinforcement Learning for Multi-Task EnvironmentsCode0
Hierarchical Cooperative Multi-Agent Reinforcement Learning with Skill DiscoveryCode0
Heuristics, Answer Set Programming and Markov Decision Process for Solving a Set of Spatial PuzzlesCode0
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Benchmark Results

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