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 851–860 of 15113 papers

TitleStatusHype
Hierarchical and Modular Network on Non-prehensile Manipulation in General Environments—0
R1-T1: Fully Incentivizing Translation Capability in LLMs via Reasoning Learning—0
CarPlanner: Consistent Auto-regressive Trajectory Planning for Large-scale Reinforcement Learning in Autonomous Driving—0
Accelerating Model-Based Reinforcement Learning with State-Space World Models—0
On the Importance of Reward Design in Reinforcement Learning-based Dynamic Algorithm Configuration: A Case Study on OneMax with (1+(λ,λ))-GACode0
Robust Gymnasium: A Unified Modular Benchmark for Robust Reinforcement Learning—0
Improving the Efficiency of a Deep Reinforcement Learning-Based Power Management System for HPC Clusters Using Curriculum Learning—0
AutoBS: Autonomous Base Station Deployment with Reinforcement Learning and Digital Network TwinsCode0
VEM: Environment-Free Exploration for Training GUI Agent with Value Environment ModelCode1
WOFOSTGym: A Crop Simulator for Learning Annual and Perennial Crop Management StrategiesCode0
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Benchmark Results

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