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 7876–7900 of 15113 papers

TitleStatusHype
Convergent and Efficient Deep Q Network AlgorithmCode0
Globally Optimal Hierarchical Reinforcement Learning for Linearly-Solvable Markov Decision ProcessesCode0
DRILL-- Deep Reinforcement Learning for Refinement Operators in ALC—0
Generalization of Reinforcement Learning with Policy-Aware Adversarial Data Augmentation—0
Learning Task Informed AbstractionsCode1
Action Set Based Policy Optimization for Safe Power Grid Management—0
Data-driven Model Predictive and Reinforcement Learning Based Control for Building Energy Management: a Survey—0
Expert Q-learning: Deep Reinforcement Learning with Coarse State Values from Offline Expert Examples—0
Causal Reinforcement Learning using Observational and Interventional DataCode1
Habitat 2.0: Training Home Assistants to Rearrange their HabitatCode2
Modularity in Reinforcement Learning via Algorithmic Independence in Credit Assignment—0
Multi-task curriculum learning in a complex, visual, hard-exploration domain: MinecraftCode1
Regret Analysis in Deterministic Reinforcement Learning—0
A Reinforcement Learning Approach for Sequential Spatial Transformer Networks—0
Concentration of Contractive Stochastic Approximation and Reinforcement Learning—0
Continuous Control with Deep Reinforcement Learning for Autonomous Vessels—0
Graph Convolutional Memory using Topological PriorsCode1
Discovering Generalizable Skills via Automated Generation of Diverse Tasks—0
Intrinsically Motivated Self-supervised Learning in Reinforcement Learning—0
Auto-Pipeline: Synthesizing Complex Data Pipelines By-Target Using Reinforcement Learning and SearchCode0
Compositional Reinforcement Learning from Logical SpecificationsCode1
Predictive Control Using Learned State Space Models via Rolling Horizon Evolution—0
Reinforcement Learning for Mean Field Games, with Applications to Economics—0
Multi-Goal Reinforcement Learning environments for simulated Franka Emika Panda robotCode1
Balancing Accuracy and Fairness for Interactive Recommendation with Reinforcement Learning—0
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Benchmark Results

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