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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 9401–9425 of 15113 papers

TitleStatusHype
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
Reinforcing the Diffusion Chain of Lateral Thought with Diffusion Language Models—0
Reinforcing User Retention in a Billion Scale Short Video Recommender System—0
Relate to Predict: Towards Task-Independent Knowledge Representations for Reinforcement Learning—0
Relational Abstractions for Generalized Reinforcement Learning on Symbolic Problems—0
Relational Deep Reinforcement Learning for Routing in Wireless Networks—0
Relational-Grid-World: A Novel Relational Reasoning Environment and An Agent Model for Relational Information Extraction—0
Relation Mention Extraction from Noisy Data with Hierarchical Reinforcement Learning—0
Relation-R1: Cognitive Chain-of-Thought Guided Reinforcement Learning for Unified Relational Comprehension—0
Relationship Explainable Multi-objective Reinforcement Learning with Semantic Explainability Generation—0
Relationship Explainable Multi-objective Optimization Via Vector Value Function Based Reinforcement Learning—0
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Benchmark Results

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