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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 1176–1200 of 15113 papers

TitleStatusHype
Bellman: A Toolbox for Model-Based Reinforcement Learning in TensorFlowCode1
An Optimistic Perspective on Offline Deep Reinforcement LearningCode1
Benchmarking Actor-Critic Deep Reinforcement Learning Algorithms for Robotics Control with Action ConstraintsCode1
Enhanced POET: Open-Ended Reinforcement Learning through Unbounded Invention of Learning Challenges and their SolutionsCode1
Benchmarking Constraint Inference in Inverse Reinforcement LearningCode1
Efficient Wasserstein Natural Gradients for Reinforcement LearningCode1
Evolutionary Population Curriculum for Scaling Multi-Agent Reinforcement LearningCode1
Autonomous Racing using a Hybrid Imitation-Reinforcement Learning ArchitectureCode1
Autonomous Reinforcement Learning: Formalism and BenchmarkingCode1
Example-guided learning of stochastic human driving policies using deep reinforcement learningCode1
AutoPhase: Compiler Phase-Ordering for High Level Synthesis with Deep Reinforcement LearningCode1
Beyond OOD State Actions: Supported Cross-Domain Offline Reinforcement LearningCode1
Beyond Pick-and-Place: Tackling Robotic Stacking of Diverse ShapesCode1
Reincarnating Reinforcement Learning: Reusing Prior Computation to Accelerate ProgressCode1
Beyond The Rainbow: High Performance Deep Reinforcement Learning on a Desktop PCCode1
Blockchain Framework for Artificial Intelligence ComputationCode1
Bidirectional Model-based Policy OptimizationCode1
Eigenoption Discovery through the Deep Successor RepresentationCode1
Efficient Symptom Inquiring and Diagnosis via Adaptive Alignment of Reinforcement Learning and ClassificationCode1
Bingham Policy Parameterization for 3D Rotations in Reinforcement LearningCode1
Widening the Pipeline in Human-Guided Reinforcement Learning with Explanation and Context-Aware Data AugmentationCode1
Exploiting Multimodal Reinforcement Learning for Simultaneous Machine TranslationCode1
Exploiting Transformer in Sparse Reward Reinforcement Learning for Interpretable Temporal Logic Motion PlanningCode1
Autonomous Exploration Under Uncertainty via Deep Reinforcement Learning on GraphsCode1
Trust Region-Based Safe Distributional Reinforcement Learning for Multiple ConstraintsCode1
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Benchmark Results

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