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 3691–3700 of 15113 papers

TitleStatusHype
Distributional Method for Risk Averse Reinforcement Learning—0
Exposure-Based Multi-Agent Inspection of a Tumbling Target Using Deep Reinforcement Learning—0
Reward Design with Language ModelsCode2
Reinforcement Learning with Depreciating Assets—0
The Provable Benefits of Unsupervised Data Sharing for Offline Reinforcement Learning—0
A Reinforcement Learning Approach for Scheduling Problems With Improved Generalization Through Order Swapping—0
Systematic Rectification of Language Models via Dead-end AnalysisCode0
Dynamic Resource Allocation for Metaverse Applications with Deep Reinforcement Learning—0
Sim-and-Real Reinforcement Learning for Manipulation: A Consensus-based Approach—0
Revolutionizing Genomics with Reinforcement Learning Techniques—0
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Benchmark Results

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