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 2001–2025 of 15113 papers

TitleStatusHype
Sample Efficient Ensemble Learning with Catalyst.RLCode1
Modeling 3D Shapes by Reinforcement LearningCode1
An empirical investigation of the challenges of real-world reinforcement learningCode1
Evolutionary Population Curriculum for Scaling Multi-Agent Reinforcement LearningCode1
Using Deep Reinforcement Learning Methods for Autonomous Vessels in 2D EnvironmentsCode1
FlapAI Bird: Training an Agent to Play Flappy Bird Using Reinforcement Learning TechniquesCode1
Robust Deep Reinforcement Learning against Adversarial Perturbations on State ObservationsCode1
Enhanced POET: Open-Ended Reinforcement Learning through Unbounded Invention of Learning Challenges and their SolutionsCode1
Social Navigation with Human Empowerment driven Deep Reinforcement LearningCode1
Simultaneous Navigation and Radio Mapping for Cellular-Connected UAV with Deep Reinforcement LearningCode1
Giving Up Control: Neurons as Reinforcement Learning AgentsCode1
Self-Supervised Discovering of Interpretable Features for Reinforcement LearningCode1
PFPN: Continuous Control of Physically Simulated Characters using Particle Filtering Policy NetworkCode1
DisCor: Corrective Feedback in Reinforcement Learning via Distribution CorrectionCode1
Deep Deterministic Portfolio OptimizationCode1
Sample Efficient Reinforcement Learning through Learning from Demonstrations in MinecraftCode1
On the Robustness of Cooperative Multi-Agent Reinforcement LearningCode1
IG-RL: Inductive Graph Reinforcement Learning for Massive-Scale Traffic Signal ControlCode1
Contention Window Optimization in IEEE 802.11ax Networks with Deep Reinforcement LearningCode1
Robust Market Making via Adversarial Reinforcement LearningCode1
Embodied Synaptic Plasticity with Online Reinforcement learningCode1
Can Increasing Input Dimensionality Improve Deep Reinforcement Learning?Code1
AutoPhase: Juggling HLS Phase Orderings in Random Forests with Deep Reinforcement LearningCode1
MVP: Unified Motion and Visual Self-Supervised Learning for Large-Scale Robotic NavigationCode1
PPMC RL Training Algorithm: Rough Terrain Intelligent Robots through Reinforcement LearningCode1
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Benchmark Results

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