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 11101–11125 of 15113 papers

TitleStatusHype
Transferring Reinforcement Learning for DC-DC Buck Converter Control via Duty Ratio Mapping: From Simulation to Implementation—0
Transfer RL across Observation Feature Spaces via Model-Based Regularization—0
Transfer Value or Policy? A Value-centric Framework Towards Transferrable Continuous Reinforcement Learning—0
Transfer with Model Features in Reinforcement Learning—0
Transformation Coding: Simple Objectives for Equivariant Representations—0
Transformer-Based Fault-Tolerant Control for Fixed-Wing UAVs Using Knowledge Distillation and In-Context Adaptation—0
Transformer Based Reinforcement Learning For Games—0
Transformer Network-based Reinforcement Learning Method for Power Distribution Network (PDN) Optimization of High Bandwidth Memory (HBM)—0
Transformers are Meta-Reinforcement Learners—0
Transformers as Game Players: Provable In-context Game-playing Capabilities of Pre-trained Models—0
Transformers in Reinforcement Learning: A Survey—0
Transforming Cooling Optimization for Green Data Center via Deep Reinforcement Learning—0
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
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Benchmark Results

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