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 2126–2150 of 15113 papers

TitleStatusHype
Reinforcement Learning with Convex ConstraintsCode1
Split Q Learning: Reinforcement Learning with Two-Stream RewardsCode1
Unsupervised Learning of Object Keypoints for Perception and ControlCode1
When to Trust Your Model: Model-Based Policy OptimizationCode1
MoËT: Mixture of Expert Trees and its Application to Verifiable Reinforcement LearningCode1
When to use parametric models in reinforcement learning?Code1
Boosting Soft Actor-Critic: Emphasizing Recent Experience without Forgetting the PastCode1
Coordinated Exploration via Intrinsic Rewards for Multi-Agent Reinforcement LearningCode1
Snooping Attacks on Deep Reinforcement LearningCode1
SQIL: Imitation Learning via Reinforcement Learning with Sparse RewardsCode1
Adversarial Policies: Attacking Deep Reinforcement LearningCode1
Maximum Entropy-Regularized Multi-Goal Reinforcement LearningCode1
QTRAN: Learning to Factorize with Transformation for Cooperative Multi-Agent Reinforcement LearningCode1
Challenges of Real-World Reinforcement LearningCode1
Model-free Deep Reinforcement Learning for Urban Autonomous DrivingCode1
Optimization Methods for Interpretable Differentiable Decision Trees in Reinforcement LearningCode1
Learning to Paint With Model-based Deep Reinforcement LearningCode1
Skew-Fit: State-Covering Self-Supervised Reinforcement LearningCode1
Model Primitive Hierarchical Lifelong Reinforcement LearningCode1
A Cooperative Multi-Agent Reinforcement Learning Framework for Resource Balancing in Complex Logistics NetworkCode1
Verifiably Safe Off-Model Reinforcement LearningCode1
CrossQ: Batch Normalization in Deep Reinforcement Learning for Greater Sample Efficiency and SimplicityCode1
The StarCraft Multi-Agent ChallengeCode1
Certified Reinforcement Learning with Logic GuidanceCode1
Learning agile and dynamic motor skills for legged robotsCode1
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Benchmark Results

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