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 11501–11525 of 15113 papers

TitleStatusHype
Identifiability in inverse reinforcement learning—0
Identifying Coordination in a Cognitive Radar Network -- A Multi-Objective Inverse Reinforcement Learning Approach—0
Identifying Critical States by the Action-Based Variance of Expected Return—0
Identifying Decision Points for Safe and Interpretable Reinforcement Learning in Hypotension Treatment—0
Identifying Reasoning Flaws in Planning-Based RL Using Tree Explanations—0
IGO-QNN: Quantum Neural Network Architecture for Inductive Grover Oracularization—0
ILAEDA: An Imitation Learning Based Approach for Automatic Exploratory Data Analysis—0
IL-flOw: Imitation Learning from Observation using Normalizing Flows—0
Illuminating Spaces: Deep Reinforcement Learning and Laser-Wall Partitioning for Architectural Layout Generation—0
Illuminating the Three Dogmas of Reinforcement Learning under Evolutionary Light—0
Image-Based Deep Reinforcement Learning with Intrinsically Motivated Stimuli: On the Execution of Complex Robotic Tasks—0
Image Captioning Based on a Hierarchical Attention Mechanism and Policy Gradient Optimization—0
Image Captioning based on Deep Reinforcement Learning—0
Image Deraining via Self-supervised Reinforcement Learning—0
Image-Guided Navigation of a Robotic Ultrasound Probe for Autonomous Spinal Sonography Using a Shadow-aware Dual-Agent Framework—0
Image quality assessment for machine learning tasks using meta-reinforcement learning—0
Image Synthesis for Data Augmentation in Medical CT using Deep Reinforcement Learning—0
Imagination-Augmented Hierarchical Reinforcement Learning for Safe and Interactive Autonomous Driving in Urban Environments—0
Imagined Value Gradients: Model-Based Policy Optimization with Transferable Latent Dynamics Models—0
Imagine Networks—0
Imitate then Transcend: Multi-Agent Optimal Execution with Dual-Window Denoise PPO—0
Imitating, Fast and Slow: Robust learning from demonstrations via decision-time planning—0
Imitating Opponent to Win: Adversarial Policy Imitation Learning in Two-player Competitive Games—0
Imitating Past Successes can be Very Suboptimal—0
SPOC: Imitating Shortest Paths in Simulation Enables Effective Navigation and Manipulation in the Real World—0
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Benchmark Results

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