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 1391–1400 of 15113 papers

TitleStatusHype
Sample Efficient Reinforcement Learning via Large Vision Language Model DistillationCode1
Exploiting Transformer in Sparse Reward Reinforcement Learning for Interpretable Temporal Logic Motion PlanningCode1
Curiosity-Driven Energy-Efficient Worker Scheduling in Vehicular Crowdsourcing: A Deep Reinforcement Learning ApproachCode1
A Relational Intervention Approach for Unsupervised Dynamics Generalization in Model-Based Reinforcement LearningCode1
Backprop-Free Reinforcement Learning with Active Neural Generative CodingCode1
Language Instructed Reinforcement Learning for Human-AI CoordinationCode1
Curious Hierarchical Actor-Critic Reinforcement LearningCode1
#Exploration: A Study of Count-Based Exploration for Deep Reinforcement LearningCode1
BabyAI 1.1Code1
Zero-Shot Compositional Policy Learning via Language GroundingCode1
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Benchmark Results

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