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 2501–2525 of 15113 papers

TitleStatusHype
Autonomous Braking System via Deep Reinforcement LearningCode0
Inter-Level Cooperation in Hierarchical Reinforcement LearningCode0
Language Model Alignment with Elastic ResetCode0
Langevin DQNCode0
Language as an Abstraction for Hierarchical Deep Reinforcement LearningCode0
Adaptively Calibrated Critic Estimates for Deep Reinforcement LearningCode0
Language Understanding for Text-based Games Using Deep Reinforcement LearningCode0
Laboratory Experiments of Model-based Reinforcement Learning for Adaptive Optics ControlCode0
L2SR: Learning to Sample and Reconstruct for Accelerated MRI via Reinforcement LearningCode0
Alpha-Mini: Minichess Agent with Deep Reinforcement LearningCode0
Automating Reinforcement Learning with Example-based ResetsCode0
L2Explorer: A Lifelong Reinforcement Learning Assessment EnvironmentCode0
LaGR-SEQ: Language-Guided Reinforcement Learning with Sample-Efficient QueryingCode0
Koopman Spectrum Nonlinear Regulators and Efficient Online LearningCode0
Kernel Density Bayesian Inverse Reinforcement LearningCode0
ALPaCA vs. GP-based Prior Learning: A Comparison between two Bayesian Meta-Learning AlgorithmsCode0
Kernel Metric Learning for In-Sample Off-Policy Evaluation of Deterministic RL PoliciesCode0
A Low Latency Adaptive Coding Spiking Framework for Deep Reinforcement LearningCode0
KEHRL: Learning Knowledge-Enhanced Language Representations with Hierarchical Reinforcement LearningCode0
Just Round: Quantized Observation Spaces Enable Memory Efficient Learning of Dynamic LocomotionCode0
A Low-Cost Ethics Shaping Approach for Designing Reinforcement Learning AgentsCode0
Jointly Pre-training with Supervised, Autoencoder, and Value Losses for Deep Reinforcement LearningCode0
Join Query Optimization with Deep Reinforcement Learning AlgorithmsCode0
A Bayesian Approach to Reinforcement Learning of Vision-Based Vehicular ControlCode0
Jointly Learning to Construct and Control Agents using Deep Reinforcement LearningCode0
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Benchmark Results

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