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 1961–1970 of 15113 papers

TitleStatusHype
Implementation Matters in Deep RL: A Case Study on PPO and TRPOCode1
RaCT: Toward Amortized Ranking-Critical Training For Collaborative FilteringCode1
Deep Symbolic Superoptimization Without Human KnowledgeCode1
Logic and the 2-Simplicial TransformerCode1
Learning Collaborative Agents with Rule Guidance for Knowledge Graph ReasoningCode1
Reinforcement Learning with Augmented DataCode1
Actor-Critic Reinforcement Learning for Control with Stability GuaranteeCode1
Hierarchical Reinforcement Learning for Automatic Disease DiagnosisCode1
Image Augmentation Is All You Need: Regularizing Deep Reinforcement Learning from PixelsCode1
Transferable Active Grasping and Real Embodied DatasetCode1
Show:102550
← PrevPage 197 of 1512Next →

Benchmark Results

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