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Distributed Map Classification using Local Observations
We consider the problem of classifying a map using a team of communicating robots.
Hierarchical Reinforcement Learning for Deep Goal Reasoning: An Expressiveness Analysis
Hierarchical DQN (h-DQN) is a two-level architecture of feedforward neural networks where the meta level selects goals and the lower level takes actions to achieve the goals.
Hierarchical Reinforcement Learning
reinforcement-learning
+1
A Layered Architecture for Active Perception: Image Classification using Deep Reinforcement Learning
We propose a planning and perception mechanism for a robot (agent), that can only observe the underlying environment partially, in order to solve an image classification problem.
