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Found 5 papers, 1 papers with code
Recursive Reasoning Graph for Multi-Agent Reinforcement Learning
Multi-agent reinforcement learning (MARL) provides an efficient way for simultaneously learning policies for multiple agents interacting with each other.
Multi-agent Reinforcement Learning
reinforcement-learning
+1
Reinforcement Learning for Autonomous Driving with Latent State Inference and Spatial-Temporal Relationships
Deep reinforcement learning (DRL) provides a promising way for learning navigation in complex autonomous driving scenarios.
Handling Missing Data with Graph Representation Learning
GRAPE tackles the missing data problem using a graph representation, where the observations and features are viewed as two types of nodes in a bipartite graph, and the observed feature values as edges.
Monte-Carlo Tree Search for Policy Optimization
Gradient-based methods are often used for policy optimization in deep reinforcement learning, despite being vulnerable to local optima and saddle points.
Improved Robustness and Safety for Autonomous Vehicle Control with Adversarial Reinforcement Learning
To improve efficiency and reduce failures in autonomous vehicles, research has focused on developing robust and safe learning methods that take into account disturbances in the environment.
