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 5426–5450 of 15113 papers

TitleStatusHype
CRC-RL: A Novel Visual Feature Representation Architecture for Unsupervised Reinforcement LearningCode0
Enabling surrogate-assisted evolutionary reinforcement learning via policy embedding—0
Towards interpretable quantum machine learning via single-photon quantum walks—0
Partitioning Distributed Compute Jobs with Reinforcement Learning and Graph Neural Networks—0
Scaling laws for single-agent reinforcement learning—0
Skill Decision TransformerCode0
Scalable Grid-Aware Dynamic Matching using Deep Reinforcement Learning—0
Scheduling Inference Workloads on Distributed Edge Clusters with Reinforcement Learning—0
Planning Multiple Epidemic Interventions with Reinforcement LearningCode0
STEEL: Singularity-aware Reinforcement Learning—0
V2N Service Scaling with Deep Reinforcement Learning—0
Identifying Expert Behavior in Offline Training Datasets Improves Behavioral Cloning of Robotic Manipulation PoliciesCode0
Regret Bounds for Markov Decision Processes with Recursive Optimized Certainty Equivalents—0
PAC-Bayesian Soft Actor-Critic LearningCode0
Transferring Multiple Policies to Hotstart Reinforcement Learning in an Air Compressor Management Problem—0
Improved Regret for Efficient Online Reinforcement Learning with Linear Function Approximation—0
Importance Weighted Actor-Critic for Optimal Conservative Offline Reinforcement LearningCode0
Hierarchical Programmatic Reinforcement Learning via Learning to Compose Programs—0
A Deep Reinforcement Learning Framework for Optimizing Congestion Control in Data Centers—0
Autonomous Satellite Docking via Adaptive Optimal Output Rregulation: A Reinforcement Learning Approach—0
Sample Efficient Deep Reinforcement Learning via Local Planning—0
STEERING: Stein Information Directed Exploration for Model-Based Reinforcement Learning—0
Turbulence control in plane Couette flow using low-dimensional neural ODE-based models and deep reinforcement learning—0
SaFormer: A Conditional Sequence Modeling Approach to Offline Safe Reinforcement Learning—0
Towards Learning Rubik's Cube with N-tuple-based Reinforcement Learning—0
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Benchmark Results

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