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 9476–9500 of 15113 papers

TitleStatusHype
Kinematics-Guided Reinforcement Learning for Object-Aware 3D Ego-Pose Estimation—0
Dirichlet policies for reinforced factor portfolios—0
Hierarchical Reinforcement Learning for Relay Selection and Power Optimization in Two-Hop Cooperative Relay Network—0
Perturbation-based exploration methods in deep reinforcement learning—0
Model-based Reinforcement Learning from Signal Temporal Logic Specifications—0
What Did You Think Would Happen? Explaining Agent Behaviour Through Intended OutcomesCode0
Sample Complexity Bounds for Two Timescale Value-based Reinforcement Learning Algorithms—0
Optimizing Age of Information Through Aerial Reconfigurable Intelligent Surfaces: A Deep Reinforcement Learning Approach—0
Challenges of Applying Deep Reinforcement Learning in Dynamic Dispatching—0
Learning to Compose Hierarchical Object-Centric Controllers for Robotic Manipulation—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
Decentralized Structural-RNN for Robot Crowd Navigation with Deep Reinforcement LearningCode1
Geometric Deep Reinforcement Learning for Dynamic DAG SchedulingCode1
Deep reinforcement learning for RAN optimization and control—0
f-IRL: Inverse Reinforcement Learning via State Marginal MatchingCode1
Automated Adversary Emulation for Cyber-Physical Systems via Reinforcement Learning—0
Deep Reinforcement Learning for Navigation in AAA Video Games—0
Safe Trajectory Planning Using Reinforcement Learning for Self Driving—0
Trajectory Planning for Autonomous Vehicles Using Hierarchical Reinforcement LearningCode1
Multi-Agent Reinforcement Learning for Channel Assignment and Power Allocation in Platoon-Based C-V2X Systems—0
Reinforcement Learning for Autonomous Driving with Latent State Inference and Spatial-Temporal Relationships—0
Reinforcement Learning for Assignment problem—0
Online Sparse Reinforcement Learning—0
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Benchmark Results

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