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 5076–5100 of 15113 papers

TitleStatusHype
Reinforcement Learning with Attention that Works: A Self-Supervised Approach—0
Reinforcement Learning with Automated Auxiliary Loss Search—0
Reinforcement Learning with Bayesian Classifiers: Efficient Skill Learning from Outcome Examples—0
Reinforcement Learning with Budget-Constrained Nonparametric Function Approximation for Opportunistic Spectrum Access—0
Reinforcement Learning with Chromatic Networks for Compact Architecture Search—0
Reinforcement Learning with Chromatic Networks—0
Reinforcement Learning with Competitive Ensembles of Information-Constrained Primitives—0
Reinforcement Learning with Convolutional Reservoir Computing—0
Reinforcement learning with Demonstrations from Mismatched Task under Sparse Reward—0
Reinforcement Learning with Depreciating Assets—0
Reinforcement Learning with Differential Privacy—0
Reinforcement learning with distance-based incentive/penalty (DIP) updates for highly constrained industrial control systems—0
Reinforcement Learning with Efficient Active Feature Acquisition—0
Reinforcement Learning with Elastic Time Steps—0
Reinforcement learning with experience replay and adaptation of action dispersion—0
Reinforcement Learning with Expert Trajectory For Quantitative Trading—0
Reinforcement Learning with Ex-Post Max-Min Fairness—0
Reinforcement Learning with External Knowledge and Two-Stage Q-functions for Predicting Popular Reddit Threads—0
Reinforcement Learning with External Knowledge by using Logical Neural Networks—0
Reinforcement Learning with Feedback-modulated TD-STDP—0
Reinforcement Learning with Feedback from Multiple Humans with Diverse Skills—0
Reinforcement Learning with Feedback Graphs—0
Reinforcement Learning with Formal Performance Metrics for Quadcopter Attitude Control under Non-nominal Contexts—0
Reinforcement Learning with Function Approximation: From Linear to Nonlinear—0
Reinforcement Learning with Function-Valued Action Spaces for Partial Differential Equation Control—0
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Benchmark Results

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