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 7801–7825 of 15113 papers

TitleStatusHype
MONEYBaRL: Exploiting pitcher decision-making using Reinforcement Learning—0
Monitoring Fidelity of Online Reinforcement Learning Algorithms in Clinical Trials—0
Monte-Carlo Planning and Learning with Language Action Value Estimates—0
Monte Carlo Planning with Large Language Model for Text-Based Game Agents—0
Monte-Carlo Siamese Policy on Actor for Satellite Image Super Resolution—0
Monte Carlo Tree Search Algorithms for Risk-Aware and Multi-Objective Reinforcement Learning—0
Monte-Carlo Tree Search for Policy Optimization—0
Moody Learners -- Explaining Competitive Behaviour of Reinforcement Learning Agents—0
MOORe: Model-based Offline-to-Online Reinforcement Learning—0
Moral reinforcement learning using actual causation—0
More Efficient Exploration with Symbolic Priors on Action Sequence Equivalences—0
More Efficient Off-Policy Evaluation through Regularized Targeted Learning—0
(More) Efficient Reinforcement Learning via Posterior Sampling—0
MOReL: Model-Based Offline Reinforcement Learning—0
More Robust Doubly Robust Off-policy Evaluation—0
MoRE: Unlocking Scalability in Reinforcement Learning for Quadruped Vision-Language-Action Models—0
MOT: A Mixture of Actors Reinforcement Learning Method by Optimal Transport for Algorithmic Trading—0
MoTiAC: Multi-Objective Actor-Critics for Real-Time Bidding—0
Motion Perception in Reinforcement Learning with Dynamic Objects—0
Motion Planner Augmented Reinforcement Learning for Robot Manipulation in Obstructed Environments—0
Motion Planning by Reinforcement Learning for an Unmanned Aerial Vehicle in Virtual Open Space with Static Obstacles—0
Motion Planning for Autonomous Vehicles in the Presence of Uncertainty Using Reinforcement Learning—0
Motion Prediction on Self-driving Cars: A Review—0
MotionRL: Align Text-to-Motion Generation to Human Preferences with Multi-Reward Reinforcement Learning—0
Motivating Physical Activity via Competitive Human-Robot Interaction—0
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Benchmark Results

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