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 9376–9400 of 15113 papers

TitleStatusHype
Reinforcement Learning Within the Classical Robotics Stack: A Case Study in Robot Soccer—0
Reinforcement Learning with Intrinsic Affinity for Personalized Prosperity Management—0
Reinforcement Learning with Intrinsically Motivated Feedback Graph for Lost-sales Inventory Control—0
Reinforcement Learning with Iterative Reasoning for Merging in Dense Traffic—0
Reinforcement Learning with Knowledge Representation and Reasoning: A Brief Survey—0
Reinforcement Learning with Large Action Spaces for Neural Machine Translation—0
Reinforcement Learning with Large Action Spaces for Neural Machine Translation—0
Reinforcement Learning with Lookahead Information—0
Reinforcement Learning with LTL and ω-Regular Objectives via Optimality-Preserving Translation to Average Rewards—0
Reinforcement Learning with Multiple Experts: A Bayesian Model Combination Approach—0
Reinforcement Learning with Neural Radiance Fields—0
Reinforcement Learning with Non-Exponential Discounting—0
Reinforcement Learning with Non-uniform State Representations for Adaptive Search—0
Reinforcement Learning without Ground-Truth State—0
Reinforcement Learning with Partial Parametric Model Knowledge—0
Reinforcement Learning with Policy Mixture Model for Temporal Point Processes Clustering—0
Reinforcement Learning with Predictive Consistent Representations—0
Reinforcement Learning with Probabilistic Guarantees for Autonomous Driving—0
Reinforcement Learning with Probabilistic Boolean Network Models of Smart Grid Devices—0
Reinforcement Learning with Probabilistically Complete Exploration—0
Mining for Potent Inhibitors through Artificial Intelligence and Physics: A Unified Methodology for Ligand Based and Structure Based Drug Design—0
Reinforcement learning with restrictions on the action set—0
Reinforcement Learning with Segment Feedback—0
Reinforcement Learning with Simple Sequence Priors—0
Reinforcement Learning with Stepwise Fairness Constraints—0
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Benchmark Results

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