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

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Showing 1941–1950 of 15113 papers

TitleStatusHype
Diffusion-based Reinforcement Learning for Dynamic UAV-assisted Vehicle Twins Migration in Vehicular Metaverses—0
Optimizing Automatic Differentiation with Deep Reinforcement Learning—0
Sim-to-Real Transfer of Deep Reinforcement Learning Agents for Online Coverage Path Planning—0
Primitive Agentic First-Order Optimization—0
Proofread: Fixes All Errors with One Tap—0
Strategically Conservative Q-LearningCode1
Excluding the Irrelevant: Focusing Reinforcement Learning through Continuous Action Masking—0
Breeding Programs Optimization with Reinforcement Learning—0
Optimizing Autonomous Driving for Safety: A Human-Centric Approach with LLM-Enhanced RLHF—0
Deterministic Uncertainty Propagation for Improved Model-Based Offline Reinforcement LearningCode0
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Benchmark Results

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