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 6876–6900 of 15113 papers

TitleStatusHype
Transforming Multimodal Models into Action Models for Radiotherapy—0
Transform then Explore: a Simple and Effective Technique for Exploratory Combinatorial Optimization with Reinforcement Learning—0
Transmit Power Control for Indoor Small Cells: A Method Based on Federated Reinforcement Learning—0
Transparency and Explanation in Deep Reinforcement Learning Neural Networks—0
Transportation-Inequalities, Lyapunov Stability and Sampling for Dynamical Systems on Continuous State Space—0
Tree-Structured Reinforcement Learning for Sequential Object Localization—0
Trends in Neural Architecture Search: Towards the Acceleration of Search—0
Triangular Dropout: Variable Network Width without Retraining—0
Triangular Dropout: Variable Network Width without Retraining—0
TrojanForge: Generating Adversarial Hardware Trojan Examples Using Reinforcement Learning—0
Truncated Emphatic Temporal Difference Methods for Prediction and Control—0
Truncated Horizon Policy Search: Combining Reinforcement Learning & Imitation Learning—0
Truncating Trajectories in Monte Carlo Reinforcement Learning—0
Truncating Trajectories in Monte Carlo Policy Evaluation: an Adaptive Approach—0
Trust-based Consensus in Multi-Agent Reinforcement Learning Systems—0
Trust-PCL: An Off-Policy Trust Region Method for Continuous Control—0
Trust the Model When It Is Confident: Masked Model-based Actor-Critic—0
Trustworthy Federated Learning via Blockchain—0
Trustworthy Reinforcement Learning Against Intrinsic Vulnerabilities: Robustness, Safety, and Generalizability—0
Tsallis Reinforcement Learning: A Unified Framework for Maximum Entropy Reinforcement Learning—0
t-Soft Update of Target Network for Deep Reinforcement Learning—0
Tuning computer vision models with task rewards—0
Tuning Mixed Input Hyperparameters on the Fly for Efficient Population Based AutoRL—0
Tuning Path Tracking Controllers for Autonomous Cars Using Reinforcement Learning—0
Turbulence control in plane Couette flow using low-dimensional neural ODE-based models and deep reinforcement learning—0
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Benchmark Results

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