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 2601–2625 of 15113 papers

TitleStatusHype
A User Simulator for Task-Completion DialoguesCode0
ALBA : Reinforcement Learning for Video Object SegmentationCode0
IxDRL: A Novel Explainable Deep Reinforcement Learning Toolkit based on Analyses of InterestingnessCode0
Iterative Reward Shaping using Human Feedback for Correcting Reward MisspecificationCode0
Is Value Functions Estimation with Classification Plug-and-play for Offline Reinforcement Learning?Code0
Is Vanilla Policy Gradient Overlooked? Analyzing Deep Reinforcement Learning for HanabiCode0
IRLAS: Inverse Reinforcement Learning for Architecture SearchCode0
Adaptive Data Exploitation in Deep Reinforcement LearningCode0
Iroko: A Framework to Prototype Reinforcement Learning for Data Center Traffic ControlCode0
A Laplacian Framework for Option Discovery in Reinforcement LearningCode0
Is Deep Reinforcement Learning Really Superhuman on Atari? Leveling the playing fieldCode0
A Unified Framework for Alternating Offline Model Training and Policy LearningCode0
Adaptive Curriculum Generation from Demonstrations for Sim-to-Real Visuomotor ControlCode0
Augmenting Replay in World Models for Continual Reinforcement LearningCode0
A Kernel Loss for Solving the Bellman EquationCode0
Is Feedback All You Need? Leveraging Natural Language Feedback in Goal-Conditioned Reinforcement LearningCode0
Adaptive coordination of working-memory and reinforcement learning in non-human primates performing a trial-and-error problem solving taskCode0
Invariant Transform Experience Replay: Data Augmentation for Deep Reinforcement LearningCode0
Inverse reinforcement learning for video gamesCode0
Intrinsic Rewards from Self-Organizing Feature Maps for Exploration in Reinforcement LearningCode0
A Joint Imitation-Reinforcement Learning Framework for Reduced Baseline RegretCode0
AIXIjs: A Software Demo for General Reinforcement LearningCode0
Inverse Reinforcement Learning in Contextual MDPsCode0
Augmented Q Imitation Learning (AQIL)Code0
Interactive Semantic Parsing for If-Then Recipes via Hierarchical Reinforcement LearningCode0
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Benchmark Results

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