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 10076–10100 of 15113 papers

TitleStatusHype
Model-based Reinforcement Learning from Signal Temporal Logic Specifications—0
Perturbation-based exploration methods in deep reinforcement learning—0
What Did You Think Would Happen? Explaining Agent Behaviour Through Intended OutcomesCode0
Hierarchical Reinforcement Learning for Relay Selection and Power Optimization in Two-Hop Cooperative Relay Network—0
Dirichlet policies for reinforced factor portfolios—0
Kinematics-Guided Reinforcement Learning for Object-Aware 3D Ego-Pose Estimation—0
Deep reinforcement learning for RAN optimization and control—0
Automated Adversary Emulation for Cyber-Physical Systems via Reinforcement Learning—0
Learning to Compose Hierarchical Object-Centric Controllers for Robotic Manipulation—0
Deep Reinforcement Learning for Navigation in AAA Video Games—0
Challenges of Applying Deep Reinforcement Learning in Dynamic Dispatching—0
Combining Propositional Logic Based Decision Diagrams with Decision Making in Urban Systems—0
Behavior Planning at Urban Intersections through Hierarchical Reinforcement Learning—0
On Function Approximation in Reinforcement Learning: Optimism in the Face of Large State Spaces—0
Reinforcement Learning for Autonomous Driving with Latent State Inference and Spatial-Temporal Relationships—0
Safe Trajectory Planning Using Reinforcement Learning for Self Driving—0
Multi-Agent Reinforcement Learning for Channel Assignment and Power Allocation in Platoon-Based C-V2X Systems—0
Optimizing Age of Information Through Aerial Reconfigurable Intelligent Surfaces: A Deep Reinforcement Learning Approach—0
On the role of planning in model-based deep reinforcement learning—0
Reliable Off-policy Evaluation for Reinforcement Learning—0
Reinforcement Learning for Assignment problem—0
Sparse Feature Selection Makes Batch Reinforcement Learning More Sample Efficient—0
Online Sparse Reinforcement Learning—0
Exploring market power using deep reinforcement learning for intelligent bidding strategies—0
Universal Activation Function For Machine Learning—0
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Benchmark Results

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