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 10501–10550 of 15113 papers

TitleStatusHype
Online Robust Reinforcement Learning with Model Uncertainty—0
Online Safety Assurance for Deep Reinforcement Learning—0
Online Service Provisioning in NFV-enabled Networks Using Deep Reinforcement Learning—0
Online Shielding for Reinforcement Learning—0
Online Sparse Reinforcement Learning—0
Asymptotic Analysis of Sample-averaged Q-learning—0
Online Sub-Sampling for Reinforcement Learning with General Function Approximation—0
Online Target Q-learning with Reverse Experience Replay: Efficiently finding the Optimal Policy for Linear MDPs—0
Online Transfer Learning in Reinforcement Learning Domains—0
Online Tuning for Offline Decentralized Multi-Agent Reinforcement Learning—0
Online Weighted Q-Ensembles for Reduced Hyperparameter Tuning in Reinforcement Learning—0
On Lottery Tickets and Minimal Task Representations in Deep Reinforcement Learning—0
On Lower Bounds for Regret in Reinforcement Learning—0
On mechanisms for transfer using landmark value functions in multi-task lifelong reinforcement learning—0
On Memory Mechanism in Multi-Agent Reinforcement Learning—0
On Modeling Long-Term User Engagement from Stochastic Feedback—0
On Multi-Agent Learning in Team Sports Games—0
On Multi-objective Policy Optimization as a Tool for Reinforcement Learning: Case Studies in Offline RL and Finetuning—0
On Neural Consolidation for Transfer in Reinforcement Learning—0
Towards Tractable Optimism in Model-Based Reinforcement Learning—0
On Optimistic versus Randomized Exploration in Reinforcement Learning—0
Sub-Goal Trees -- a Framework for Goal-Directed Trajectory Prediction and Optimization—0
Detecting Small Query Graphs in A Large Graph via Neural Subgraph Search—0
Subjective Reinforcement Learning for Open Complex Environments—0
Sublinear Least-Squares Value Iteration via Locality Sensitive Hashing—0
Sublinear Regret for a Class of Continuous-Time Linear-Quadratic Reinforcement Learning Problems—0
Sublinear Regret for Learning POMDPs—0
Suboptimal and trait-like reinforcement learning strategies correlate with midbrain encoding of prediction errors—0
Sub-optimal Policy Aided Multi-Agent Reinforcement Learning for Flocking Control—0
Sub-policy Adaptation for Hierarchical Reinforcement Learning—0
Sub-policy Adaptation for Hierarchical Reinforcement Learning—0
Subtask-Aware Visual Reward Learning from Segmented Demonstrations—0
Sub-Task Discovery with Limited Supervision: A Constrained Clustering Approach—0
Successive Over Relaxation Q-Learning—0
Successor Feature Neural Episodic Control—0
Successor Features for Transfer in Reinforcement Learning—0
Successor Features Combine Elements of Model-Free and Model-based Reinforcement Learning—0
Successor Options : An Option Discovery Algorithm for Reinforcement Learning—0
Success-Rate Targeted Reinforcement Learning by Disorientation Penalty—0
SUMBT+LaRL: Effective Multi-domain End-to-end Neural Task-oriented Dialog System—0
Summarising and Comparing Agent Dynamics with Contrastive Spatiotemporal Abstraction—0
SUMO: Search-Based Uncertainty Estimation for Model-Based Offline Reinforcement Learning—0
Super-Human Performance in Gran Turismo Sport Using Deep Reinforcement Learning—0
Superior Computer Chess with Model Predictive Control, Reinforcement Learning, and Rollout—0
Superior Performance with Diversified Strategic Control in FPS Games Using General Reinforcement Learning—0
Superkernel Neural Architecture Search for Image Denoising—0
SuperPADL: Scaling Language-Directed Physics-Based Control with Progressive Supervised Distillation—0
Superstition in the Network: Deep Reinforcement Learning Plays Deceptive Games—0
Supervised Advantage Actor-Critic for Recommender Systems—0
Supervised and Reinforcement Learning from Observations in Reconnaissance Blind Chess—0
Show:102550
← PrevPage 211 of 303Next →

Benchmark Results

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