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 12251–12300 of 15113 papers

TitleStatusHype
Search from History and Reason for Future: Two-stage Reasoning on Temporal Knowledge Graphs—0
Searching for High-Value Molecules Using Reinforcement Learning and Transformers—0
Searching Learning Strategy with Reinforcement Learning for 3D Medical Image Segmentation—0
Search on the Replay Buffer: Bridging Planning and Reinforcement Learning—0
Second Thoughts are Best: Learning to Re-Align With Human Values from Text Edits—0
SECRM-2D: RL-Based Efficient and Comfortable Route-Following Autonomous Driving with Analytic Safety Guarantees—0
Secure Computation Offloading in Blockchain based IoT Networks with Deep Reinforcement Learning—0
Security Analysis of Safe and Seldonian Reinforcement Learning Algorithms—0
Security-Aware Virtual Network Embedding Algorithm based on Reinforcement Learning—0
SeedNet: Automatic Seed Generation With Deep Reinforcement Learning for Robust Interactive Segmentation—0
Seeing by haptic glance: reinforcement learning-based 3D object Recognition—0
Seeing-Eye Quadruped Navigation with Force Responsive Locomotion Control—0
Seeing is not Believing: Robust Reinforcement Learning against Spurious Correlation—0
Seeking Visual Discomfort: Curiosity-driven Representations for Reinforcement Learning—0
SeekNet: Improved Human Instance Segmentation and Tracking via Reinforcement Learning Based Optimized Robot Relocation—0
SEERL: Sample Efficient Ensemble Reinforcement Learning—0
Segmenting Action-Value Functions Over Time-Scales in SARSA via TD(Δ)—0
Segregation Dynamics with Reinforcement Learning and Agent Based Modeling—0
SEIHAI: A Sample-efficient Hierarchical AI for the MineRL Competition—0
Select before Act: Spatially Decoupled Action Repetition for Continuous Control—0
Selecting Mechanical Parameters of a Monopode Jumping System with Reinforcement Learning—0
Selecting Near-Optimal Approximate State Representations in Reinforcement Learning—0
Selecting the State-Representation in Reinforcement Learning—0
Selective Credit Assignment—0
Selective Experience Sharing in Reinforcement Learning Enhances Interference Management—0
Selective Particle Attention: Visual Feature-Based Attention in Deep Reinforcement Learning—0
Selective Pseudo-Labeling with Reinforcement Learning for Semi-Supervised Domain Adaptation—0
Selective Reviews of Bandit Problems in AI via a Statistical View—0
Selective Token Generation for Few-shot Language Modeling—0
Selective Transfer with Reinforced Transfer Network for Partial Domain Adaptation—0
Selective Uncertainty Propagation in Offline RL—0
Selector-Enhancer: Learning Dynamic Selection of Local and Non-local Attention Operation for Speech Enhancement—0
Self-Adapting Goals Allow Transfer of Predictive Models to New Tasks—0
Self-Awareness Safety of Deep Reinforcement Learning in Road Traffic Junction Driving—0
Self-Confirming Transformer for Belief-Conditioned Adaptation in Offline Multi-Agent Reinforcement Learning—0
Self-Consistent Models and Values—0
Self-Consistent Trajectory Autoencoder: Hierarchical Reinforcement Learning with Trajectory Embeddings—0
Self-Critical Alternate Learning based Semantic Broadcast Communication—0
Self-critical Sequence Training for Automatic Speech Recognition—0
Self-Driving Car Racing: Application of Deep Reinforcement Learning—0
Self-driving scale car trained by Deep reinforcement learning—0
Self-Driving Telescopes: Autonomous Scheduling of Astronomical Observation Campaigns with Offline Reinforcement Learning—0
Self-evolving Autoencoder Embedded Q-Network—0
Self-Evolving Curriculum for LLM Reasoning—0
Self-Imitation Advantage Learning—0
Self-Imitation Learning by Planning—0
Self-Imitation Learning from Demonstrations—0
Self-Improving Robots: End-to-End Autonomous Visuomotor Reinforcement Learning—0
Self-Inspection Method of Unmanned Aerial Vehicles in Power Plants Using Deep Q-Network Reinforcement Learning—0
Self-Learned Formula Synthesis in Set Theory—0
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Benchmark Results

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