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 5526–5550 of 15113 papers

TitleStatusHype
Deep Constrained Q-learning—0
Interpretable Off-Policy Evaluation in Reinforcement Learning by Highlighting Influential Transitions—0
Interpretable Option Discovery using Deep Q-Learning and Variational Autoencoders—0
Interpretable performance analysis towards offline reinforcement learning: A dataset perspective—0
Interpretable pipelines with evolutionarily optimized modules for RL tasks with visual inputs—0
Interpretable Policies for Reinforcement Learning by Genetic Programming—0
Natural Language Specification of Reinforcement Learning Policies through Differentiable Decision Trees—0
Interpretable Preference-based Reinforcement Learning with Tree-Structured Reward Functions—0
Interpretable Rationale Augmented Charge Prediction System—0
Interpretable Recognition of Fused Magnesium Furnace Working Conditions with Deep Convolutional Stochastic Configuration Networks—0
Interpretable Reinforcement Learning for Load Balancing using Kolmogorov-Arnold Networks—0
Interpretable Reinforcement Learning Inspired by Piaget's Theory of Cognitive Development—0
Interpretable Reinforcement Learning via Neural Additive Models for Inventory Management—0
Interpretable Reinforcement Learning with Ensemble Methods—0
Interpretable Reinforcement Learning With Neural Symbolic Logic—0
Interpretable Reinforcement Learning with Multilevel Subgoal Discovery—0
Interpretable Stochastic Model Predictive Control using Distributional Reinforced Estimation for Quadrotor Tracking Systems—0
Interpretable UAV Collision Avoidance using Deep Reinforcement Learning—0
Interpreting Graph Drawing with Multi-Agent Reinforcement Learning—0
Interpreting Reinforcement Policies through Local Behaviors—0
Intersectional Fairness in Reinforcement Learning with Large State and Constraint Spaces—0
Interval Estimation for Reinforcement-Learning Algorithms in Continuous-State Domains—0
Intervention Aided Reinforcement Learning for Safe and Practical Policy Optimization in Navigation—0
Int-HRL: Towards Intention-based Hierarchical Reinforcement Learning—0
Intrinsically Guided Exploration in Meta Reinforcement Learning—0
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Benchmark Results

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