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 5901–5925 of 15113 papers

TitleStatusHype
Offline Reinforcement Learning from Human Feedback in Real-World Sequence-to-Sequence Tasks—0
Learning from Longitudinal Face Demonstration - Where Tractable Deep Modeling Meets Inverse Reinforcement Learning—0
Learning from Outside the Viability Kernel: Why we Should Build Robots that can Fall with Grace—0
Learning from Peers: Deep Transfer Reinforcement Learning for Joint Radio and Cache Resource Allocation in 5G RAN Slicing—0
Learning from Reward-Free Offline Data: A Case for Planning with Latent Dynamics Models—0
Learning from Simulation, Racing in Reality—0
Learning from Suboptimal Data in Continuous Control via Auto-Regressive Soft Q-Network—0
Learning from Symmetry: Meta-Reinforcement Learning with Symmetrical Behaviors and Language Instructions—0
Learning from Teaching Regularization: Generalizable Correlations Should be Easy to Imitate—0
Learning Functionally Decomposed Hierarchies for Continuous Navigation Tasks—0
Learning Future Representation with Synthetic Observations for Sample-efficient Reinforcement Learning—0
Learning Gaussian Policies from Smoothed Action Value Functions—0
Learning Generalizable Agents via Saliency-Guided Features Decorrelation—0
Learning Generalized Wireless MAC Communication Protocols via Abstraction—0
Learning General-Purpose Controllers via Locally Communicating Sensorimotor Modules—0
Learning General World Models in a Handful of Reward-Free Deployments—0
Learning Goal-Conditioned Value Functions with one-step Path rewards rather than Goal-Rewards—0
Learning Goal-Oriented Visual Dialog Agents: Imitating and Surpassing Analytic Experts—0
Learning Good Policies By Learning Good Perceptual Models—0
Learning Good Representation via Continuous Attention—0
LearningGroup: A Real-Time Sparse Training on FPGA via Learnable Weight Grouping for Multi-Agent Reinforcement Learning—0
Learning Heuristics for Automated Reasoning through Reinforcement Learning—0
Learning Heuristics for Quantified Boolean Formulas through Reinforcement Learning—0
Learning Hierarchical Structures On-The-Fly with a Recurrent-Recursive Model for Sequences—0
Learning how to Interact with a Complex Interface using Hierarchical Reinforcement Learning—0
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Benchmark Results

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