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

Showing 461470 of 15113 papers

TitleStatusHype
Cross-Modal Domain Adaptation for Reinforcement LearningCode1
Cross-modal Domain Adaptation for Cost-Efficient Visual Reinforcement LearningCode1
A Benchmark Environment for Offline Reinforcement Learning in Racing GamesCode1
A Deep Reinforced Model for Zero-Shot Cross-Lingual Summarization with Bilingual Semantic Similarity RewardsCode1
CTDS: Centralized Teacher with Decentralized Student for Multi-Agent Reinforcement LearningCode1
Contrastive Retrospection: honing in on critical steps for rapid learning and generalization in RLCode1
A Benchmark Environment Motivated by Industrial Control ProblemsCode1
A Closer Look at Advantage-Filtered Behavioral Cloning in High-Noise DatasetsCode1
CURL: Contrastive Unsupervised Representation Learning for Reinforcement LearningCode1
Contrastive State Augmentations for Reinforcement Learning-Based Recommender SystemsCode1
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Benchmark Results

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