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 3461–3470 of 15113 papers

TitleStatusHype
Zero-Shot Generalization of Vision-Based RL Without Data Augmentation—0
Crafting desirable climate trajectories with RL explored socio-environmental simulationsCode0
MotionRL: Align Text-to-Motion Generation to Human Preferences with Multi-Reward Reinforcement Learning—0
A Safety Modulator Actor-Critic Method in Model-Free Safe Reinforcement Learning and Application in UAV Hovering—0
Q-WSL: Optimizing Goal-Conditioned RL with Weighted Supervised Learning via Dynamic Programming—0
Solving Multi-Goal Robotic Tasks with Decision Transformer—0
Reinforcement Learning From Imperfect Corrective Actions And Proxy Rewards—0
Solving robust MDPs as a sequence of static RL problems—0
On the Modeling Capabilities of Large Language Models for Sequential Decision Making—0
AlphaRouter: Quantum Circuit Routing with Reinforcement Learning and Tree Search—0
Show:102550
← PrevPage 347 of 1512Next →

Benchmark Results

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