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 1971–1980 of 15113 papers

TitleStatusHype
Cross-Embodiment Robot Manipulation Skill Transfer using Latent Space AlignmentCode1
FightLadder: A Benchmark for Competitive Multi-Agent Reinforcement Learning—0
MOT: A Mixture of Actors Reinforcement Learning Method by Optimal Transport for Algorithmic Trading—0
Reinforcement Learning as a Robotics-Inspired Framework for Insect Navigation: From Spatial Representations to Neural Implementation—0
When to Sense and Control? A Time-adaptive Approach for Continuous-Time RLCode0
Combinatorial Multivariant Multi-Armed Bandits with Applications to Episodic Reinforcement Learning and Beyond—0
Causal prompting model-based offline reinforcement learning—0
NeoRL: Efficient Exploration for Nonepisodic RL—0
A Fast Convergence Theory for Offline Decision Making—0
Federated Learning-based Collaborative Wideband Spectrum Sensing and Scheduling for UAVs in UTM Systems—0
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Benchmark Results

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