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 5151–5175 of 15113 papers

TitleStatusHype
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
Relative Distributed Formation and Obstacle Avoidance with Multi-agent Reinforcement Learning—0
Relative Importance Sampling for off-Policy Actor-Critic in Deep Reinforcement Learning—0
Relative Policy-Transition Optimization for Fast Policy Transfer—0
A Transfer Learning Approach to Minimize Reinforcement Learning Risks in Energy Optimization for Smart Buildings—0
RELDEC: Reinforcement Learning-Based Decoding of Moderate Length LDPC Codes—0
ReLeaSER: A Reinforcement Learning Strategy for Optimizing Utilization Of Ephemeral Cloud Resources—0
ReLeQ: A Reinforcement Learning Approach for Deep Quantization of Neural Networks—0
Reliable Critics: Monotonic Improvement and Convergence Guarantees for Reinforcement Learning—0
Reliable Off-policy Evaluation for Reinforcement Learning—0
Reliable validation of Reinforcement Learning Benchmarks—0
Fully Autonomous Real-World Reinforcement Learning with Applications to Mobile Manipulation—0
ReLMoGen: Leveraging Motion Generation in Reinforcement Learning for Mobile Manipulation—0
ReLOAD: Reinforcement Learning with Optimistic Ascent-Descent for Last-Iterate Convergence in Constrained MDPs—0
REMEDI: REinforcement learning-driven adaptive MEtabolism modeling of primary sclerosing cholangitis DIsease progression—0
Remember and Forget Experience Replay for Multi-Agent Reinforcement Learning—0
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Benchmark Results

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