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 5126–5150 of 15113 papers

TitleStatusHype
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
Reinforcement Learning with Structured Hierarchical Grammar Representations of Actions—0
Reinforcement Learning with Subspaces using Free Energy Paradigm—0
Reinforcement Learning with Supervision from Noisy Demonstrations—0
Reinforcement Learning With Temporal Logic Rewards—0
Reinforcement Learning with Temporal-Logic-Based Causal Diagrams—0
Reinforcement Learning with Time-dependent Goals for Robotic Musicians—0
Reinforcement Learning with Trajectory Feedback—0
Reinforcement Learning with Unbiased Policy Evaluation and Linear Function Approximation—0
Reinforcement Learning with Uncertainty Estimation for Tactical Decision-Making in Intersections—0
Reinforcement Learning Your Way: Agent Characterization through Policy Regularization—0
Reinforcement Pre-Training—0
Reinforcement Speculative Decoding for Fast Ranking—0
Reinforce Security: A Model-Free Approach Towards Secure Wiretap Coding—0
Single-step Options for Adversary Driving—0
Reinforcing Semantic-Symmetry for Document Summarization—0
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Benchmark Results

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