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 6351–6375 of 15113 papers

TitleStatusHype
Symmetry-aware Neural Architecture for Embodied Visual Navigation—0
Symmetry-Aware Neural Architecture for Embodied Visual Exploration—0
Symmetry Detection in Trajectory Data for More Meaningful Reinforcement Learning Representations—0
Symmetry Learning for Function Approximation in Reinforcement Learning—0
Symmetry reduction for deep reinforcement learning active control of chaotic spatiotemporal dynamics—0
Synchronous vs Asynchronous Reinforcement Learning in a Real World Robot—0
Synergistic Formulaic Alpha Generation for Quantitative Trading based on Reinforcement Learning—0
Synergizing AI and Digital Twins for Next-Generation Network Optimization, Forecasting, and Security—0
Synthesizing Chemical Plant Operation Procedures using Knowledge, Dynamic Simulation and Deep Reinforcement Learning—0
Synthesizing Programmatic Policies that Inductively Generalize—0
Synthesizing Safe Policies under Probabilistic Constraints with Reinforcement Learning and Bayesian Model Checking—0
Synthesizing world models for bilevel planning—0
Synthetic Acute Hypotension and Sepsis Datasets Based on MIMIC-III and Published as Part of the Health Gym Project—0
Synthetic Health-related Longitudinal Data with Mixed-type Variables Generated using Diffusion Models—0
Synthetic Sample Selection via Reinforcement Learning—0
SySLLM: Generating Synthesized Policy Summaries for Reinforcement Learning Agents Using Large Language Models—0
Systematic Generalisation through Task Temporal Logic and Deep Reinforcement Learning—0
System Design for an Integrated Lifelong Reinforcement Learning Agent for Real-Time Strategy Games—0
Systems Theoretic Process Analysis of a Run Time Assured Neural Network Control System—0
Tabular and Deep Reinforcement Learning for Gittins Index—0
Tackling Heavy-Tailed Rewards in Reinforcement Learning with Function Approximation: Minimax Optimal and Instance-Dependent Regret Bounds—0
Model-based Offline Reinforcement Learning with Lower Expectile Q-Learning—0
Tackling Morpion Solitaire with AlphaZero-likeRanked Reward Reinforcement Learning—0
Tackling Real-World Autonomous Driving using Deep Reinforcement Learning—0
Tackling the Zero-Shot Reinforcement Learning Loss Directly—0
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Benchmark Results

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