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 3741–3750 of 15113 papers

TitleStatusHype
Robust Decision Transformer: Tackling Data Corruption in Offline RL via Sequence Modeling—0
Using Petri Nets as an Integrated Constraint Mechanism for Reinforcement Learning Tasks—0
ROER: Regularized Optimal Experience ReplayCode0
Improving Sample Efficiency of Reinforcement Learning with Background Knowledge from Large Language ModelsCode0
Warm-up Free Policy Optimization: Improved Regret in Linear Markov Decision Processes—0
PWM: Policy Learning with Multi-Task World Models—0
Physics-Informed Model and Hybrid Planning for Efficient Dyna-Style Reinforcement LearningCode0
Reinforcement Learning-driven Data-intensive Workflow Scheduling for Volunteer Edge-Cloud—0
To Switch or Not to Switch? Balanced Policy Switching in Offline Reinforcement Learning—0
Safe Reinforcement Learning for Power System Control: A Review—0
Show:102550
← PrevPage 375 of 1512Next →

Benchmark Results

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