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 1271–1280 of 15113 papers

TitleStatusHype
Cross-modal Domain Adaptation for Cost-Efficient Visual Reinforcement LearningCode1
BCORLE(): An Offline Reinforcement Learning and Evaluation Framework for Coupons Allocation in E-commerce MarketCode1
Learning Long-Term Reward Redistribution via Randomized Return DecompositionCode1
Episodic Multi-agent Reinforcement Learning with Curiosity-Driven ExplorationCode1
Plan Better Amid Conservatism: Offline Multi-Agent Reinforcement Learning with Actor RectificationCode1
Generalized Decision Transformer for Offline Hindsight Information MatchingCode1
On Effective Scheduling of Model-based Reinforcement LearningCode1
Intelligent Trading Systems: A Sentiment-Aware Reinforcement Learning ApproachCode1
Resilient Consensus-based Multi-agent Reinforcement Learning with Function ApproximationCode1
User Allocation in Mobile Edge Computing: A Deep Reinforcement Learning ApproachCode1
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Benchmark Results

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