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 8051–8075 of 15113 papers

TitleStatusHype
Natural Policy Gradients In Reinforcement Learning Explained—0
Natural Value Approximators: Learning when to Trust Past Estimates—0
NavBench: A Unified Robotics Benchmark for Reinforcement Learning-Based Autonomous Navigation—0
Navigating Assistance System for Quadcopter with Deep Reinforcement Learning—0
Navigating Occluded Intersections with Autonomous Vehicles using Deep Reinforcement Learning—0
Navigating Uncertainty in ESG Investing—0
Navigating WebAI: Training Agents to Complete Web Tasks with Large Language Models and Reinforcement Learning—0
Navigational Instruction Generation as Inverse Reinforcement Learning with Neural Machine Translation—0
Navigation In Urban Environments Amongst Pedestrians Using Multi-Objective Deep Reinforcement Learning—0
NavigationNet: A Large-scale Interactive Indoor Navigation Dataset—0
Navigation of micro-robot swarms for targeted delivery using reinforcement learning—0
Navigation with QPHIL: Quantizing Planner for Hierarchical Implicit Q-Learning—0
PLANRL: A Motion Planning and Imitation Learning Framework to Bootstrap Reinforcement Learning—0
NAVREN-RL: Learning to fly in real environment via end-to-end deep reinforcement learning using monocular images—0
Near Instance-Optimal PAC Reinforcement Learning for Deterministic MDPs—0
NEARL: Non-Explicit Action Reinforcement Learning for Robotic Control—0
Nearly Horizon-Free Offline Reinforcement Learning—0
Nearly Minimax Optimal Offline Reinforcement Learning with Linear Function Approximation: Single-Agent MDP and Markov Game—0
Nearly Minimax Optimal Reinforcement Learning for Linear Mixture Markov Decision Processes—0
Nearly Minimax Optimal Reinforcement Learning with Linear Function Approximation—0
Nearly Minimax Optimal Reinforcement Learning for Linear Markov Decision Processes—0
Nearly Minimax Optimal Reward-free Reinforcement Learning—0
Nearly Optimal Policy Optimization with Stable at Any Time Guarantee—0
Near-optimal Policy Optimization Algorithms for Learning Adversarial Linear Mixture MDPs—0
Near-Minimax-Optimal Distributional Reinforcement Learning with a Generative Model—0
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Benchmark Results

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