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 8501–8525 of 15113 papers

TitleStatusHype
Privacy-Preserved Task Offloading in Mobile Blockchain with Deep Reinforcement Learning—0
Privacy Preserving Off-Policy Evaluation—0
Privacy-Preserving Reinforcement Learning Beyond Expectation—0
Privacy Preserving Reinforcement Learning for Population Processes—0
Privacy-Preserving Kickstarting Deep Reinforcement Learning with Privacy-Aware Learners—0
Privately Aligning Language Models with Reinforcement Learning—0
Private Reinforcement Learning with PAC and Regret Guarantees—0
Privileged Information Dropout in Reinforcement Learning—0
Privileged to Predicted: Towards Sensorimotor Reinforcement Learning for Urban Driving—0
PRM-RL: Long-range Robotic Navigation Tasks by Combining Reinforcement Learning and Sampling-based Planning—0
ProAPT: Projection of APT Threats with Deep Reinforcement Learning—0
Probabilistic Charging Power Forecast of EVCS: Reinforcement Learning Assisted Deep Learning Approach—0
Probabilistic Constraint for Safety-Critical Reinforcement Learning—0
Probabilistic Curriculum Learning for Goal-Based Reinforcement Learning—0
Probabilistic Guarantees for Safe Deep Reinforcement Learning—0
Probabilistic hypergraph grammars for efficient molecular optimization—0
Probabilistic Inference in Reinforcement Learning Done Right—0
Probabilistic inverse reinforcement learning in unknown environments—0
Probabilistic inverse reinforcement learning in unknown environments—0
Probabilistic Machine Learning for Healthcare—0
Probabilistic Model Checking of Stochastic Reinforcement Learning Policies—0
Probabilistic model predictive safety certification for learning-based control—0
Probabilistic Prediction of Interactive Driving Behavior via Hierarchical Inverse Reinforcement Learning—0
Probabilistic Recursive Reasoning for Multi-Agent Reinforcement Learning—0
Probabilistic Satisfaction of Temporal Logic Constraints in Reinforcement Learning via Adaptive Policy-Switching—0
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Benchmark Results

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