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 9876–9900 of 15113 papers

TitleStatusHype
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
MP3: Movement Primitive-Based (Re-)Planning Policy—0
MPC4RL -- A Software Package for Reinforcement Learning based on Model Predictive Control—0
MPC-based Reinforcement Learning for a Simplified Freight Mission of Autonomous Surface Vehicles—0
MPC-based Reinforcement Learning for Economic Problems with Application to Battery Storage—0
MQES: Max-Q Entropy Search for Efficient Exploration in Continuous Reinforcement Learning—0
MQGrad: Reinforcement Learning of Gradient Quantization in Parameter Server—0
MRAC-RL: A Framework for On-Line Policy Adaptation Under Parametric Model Uncertainty—0
MSDF: A Deep Reinforcement Learning Framework for Service Function Chain Migration—0
MS-Ranker: Accumulating Evidence from Potentially Correct Candidates for Answer Selection—0
MSRL: Distributed Reinforcement Learning with Dataflow Fragments—0
MSVIPER: Improved Policy Distillation for Reinforcement-Learning-Based Robot Navigation—0
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Benchmark Results

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