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 10676–10700 of 15113 papers

TitleStatusHype
Temporal Difference Weighted Ensemble For Reinforcement Learning—0
Temporal-Differential Learning in Continuous Environments—0
Temporal Distance-aware Transition Augmentation for Offline Model-based Reinforcement Learning—0
Temporal-Logic-Based Intermittent, Optimal, and Safe Continuous-Time Learning for Trajectory Tracking—0
Temporal-Logic-Based Reward Shaping for Continuing Reinforcement Learning Tasks—0
Temporal Logic Guided Safe Reinforcement Learning Using Control Barrier Functions—0
Temporal Logic Specification-Conditioned Decision Transformer for Offline Safe Reinforcement Learning—0
TemporalPaD: a reinforcement-learning framework for temporal feature representation and dimension reduction—0
Temporal-related Convolutional-Restricted-Boltzmann-Machine capable of learning relational order via reinforcement learning procedure?—0
Temporal-Spatial Causal Interpretations for Vision-Based Reinforcement Learning—0
SFP: State-free Priors for Exploration in Off-Policy Reinforcement Learning—0
Tensor-based Cooperative Control for Large Scale Multi-intersection Traffic Signal Using Deep Reinforcement Learning and Imitation Learning—0
TensorRL-QAS: Reinforcement learning with tensor networks for scalable quantum architecture search—0
Terminal Adaptive Guidance for Autonomous Hypersonic Strike Weapons via Reinforcement Learning—0
Terminal Prediction as an Auxiliary Task for Deep Reinforcement Learning—0
Tesseract: Tensorised Actors for Multi-Agent Reinforcement Learning—0
Test-Cost Sensitive Methods for Identifying Nearby Points—0
Testing match-3 video games with Deep Reinforcement Learning—0
Test Where Decisions Matter: Importance-driven Testing for Deep Reinforcement Learning—0
TeViR: Text-to-Video Reward with Diffusion Models for Efficient Reinforcement Learning—0
Text as Environment: A Deep Reinforcement Learning Text Readability Assessment Model—0
Text-Based Interactive Recommendation via Constraint-Augmented Reinforcement Learning—0
Text-based RL Agents with Commonsense Knowledge: New Challenges, Environments and Approaches—0
TextDiffuser-RL: Efficient and Robust Text Layout Optimization for High-Fidelity Text-to-Image Synthesis—0
Text Generation with Efficient (Soft) Q-Learning—0
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Benchmark Results

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