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 771–780 of 15113 papers

TitleStatusHype
Federated Ensemble-Directed Offline Reinforcement LearningCode1
Explainable Reinforcement Learning via a Causal World ModelCode1
Leveraging Factored Action Spaces for Efficient Offline Reinforcement Learning in HealthcareCode1
Online Portfolio Management via Deep Reinforcement Learning with High-Frequency DataCode1
X-RLflow: Graph Reinforcement Learning for Neural Network Subgraphs TransformationCode1
Contrastive Energy Prediction for Exact Energy-Guided Diffusion Sampling in Offline Reinforcement LearningCode1
DEIR: Efficient and Robust Exploration through Discriminative-Model-Based Episodic Intrinsic RewardsCode1
Bridging RL Theory and Practice with the Effective HorizonCode1
Sample-efficient Model-based Reinforcement Learning for Quantum ControlCode1
Benchmarking Actor-Critic Deep Reinforcement Learning Algorithms for Robotics Control with Action ConstraintsCode1
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Benchmark Results

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