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Found 13 papers, 5 papers with code
Towards Addressing Training Data Scarcity Challenge in Emerging Radio Access Networks: A Survey and Framework
The extensive survey of training data scarcity addressing techniques combined with proposed framework to select a suitable technique for given type of data, can assist researchers and network operators in choosing appropriate methods to overcome the data scarcity challenge in leveraging AI to radio access network automation.
Multi-agent Reinforcement Learning with Graph Q-Networks for Antenna Tuning
Future generations of mobile networks are expected to contain more and more antennas with growing complexity and more parameters.
Multi-agent Reinforcement Learning
reinforcement-learning
+1
Model Based Residual Policy Learning with Applications to Antenna Control
Non-differentiable controllers and rule-based policies are widely used for controlling real systems such as telecommunication networks and robots.
A Graph Attention Learning Approach to Antenna Tilt Optimization
GAQ relies on a graph attention mechanism to select relevant neighbors information, improve the agent state representation, and update the tilt control policy based on a history of observations using a Deep Q-Network (DQN).
Coordinated Reinforcement Learning for Optimizing Mobile Networks
In this work, we demonstrate how to use coordination graphs and reinforcement learning in a complex application involving hundreds of cooperating agents.
Multi-agent Reinforcement Learning
reinforcement-learning
+1
Reinforcement Learning with Iterative Reasoning for Merging in Dense Traffic
This approach exposes the agent to a broad variety of behaviors during training, which promotes learning policies that are robust to model discrepancies.
Point-Based Methods for Model Checking in Partially Observable Markov Decision Processes
Autonomous systems are often required to operate in partially observable environments.
Cooperation-Aware Reinforcement Learning for Merging in Dense Traffic
In this work, we present a reinforcement learning approach to learn how to interact with drivers with different cooperation levels.
Safe Reinforcement Learning with Scene Decomposition for Navigating Complex Urban Environments
Navigating urban environments represents a complex task for automated vehicles.
Pedestrian Collision Avoidance System for Scenarios with Occlusions
We show that combining the two approaches provides a robust autonomous braking system that reduces unnecessary braking caused by using the AEB system on its own.
Reinforcement Learning with Probabilistic Guarantees for Autonomous Driving
We propose a generic approach to enforce probabilistic guarantees on an RL agent.
Decomposition Methods with Deep Corrections for Reinforcement Learning
In contexts where an agent interacts with multiple entities, utility decomposition can be used to separate the global objective into local tasks considering each individual entity independently.
Belief State Planning for Autonomously Navigating Urban Intersections
Urban intersections represent a complex environment for autonomous vehicles with many sources of uncertainty.
Robotics
