5 research outputs found
The Dreaming Variational Autoencoder for Reinforcement Learning Environments
Reinforcement learning has shown great potential in generalizing over raw
sensory data using only a single neural network for value optimization. There
are several challenges in the current state-of-the-art reinforcement learning
algorithms that prevent them from converging towards the global optima. It is
likely that the solution to these problems lies in short- and long-term
planning, exploration and memory management for reinforcement learning
algorithms. Games are often used to benchmark reinforcement learning algorithms
as they provide a flexible, reproducible, and easy to control environment.
Regardless, few games feature a state-space where results in exploration,
memory, and planning are easily perceived. This paper presents The Dreaming
Variational Autoencoder (DVAE), a neural network based generative modeling
architecture for exploration in environments with sparse feedback. We further
present Deep Maze, a novel and flexible maze engine that challenges DVAE in
partial and fully-observable state-spaces, long-horizon tasks, and
deterministic and stochastic problems. We show initial findings and encourage
further work in reinforcement learning driven by generative exploration.Comment: Best Student Paper Award, Proceedings of the 38th SGAI International
Conference on Artificial Intelligence, Cambridge, UK, 2018, Artificial
Intelligence XXXV, 201
The Dreaming Variational Autoencoder for Reinforcement Learning Environments
Reinforcement learning has shown great potential in generalizing over raw sensory data using only a single neural network for value optimization. There are several challenges in the current state-of-the-art reinforcement learning algorithms that prevent them from converging towards the global optima. It is likely that the solution to these problems lies in short- and long-term planning, exploration and memory management for reinforcement learning algorithms. Games are often used to benchmark reinforcement learning algorithms as they provide a flexible, reproducible, and easy to control environment. Regardless, few games feature a state-space where results in exploration, memory, and planning are easily perceived. This paper presents The Dreaming Variational Autoencoder (DVAE), a neural network based generative modeling architecture for exploration in environments with sparse feedback. We further present Deep Maze, a novel and flexible maze engine that challenges DVAE in partial and fully-observable state-spaces, long-horizon tasks, and deterministic and stochastic problems. We show initial findings and encourage further work in reinforcement learning driven by generative exploration.The Dreaming Variational Autoencoder for Reinforcement Learning EnvironmentsacceptedVersionNivå
Towards safe reinforcement-learning in industrial grid-warehousing
publishedVersionHybri
The Dreaming Variational Autoencoder for Reinforcement Learning Environments
Reinforcement learning has shown great potential in generalizing over raw sensory data using only a single neural network for value optimization. There are several challenges in the current state-of-the-art reinforcement learning algorithms that prevent them from converging towards the global optima. It is likely that the solution to these problems lies in short- and long-term planning, exploration and memory management for reinforcement learning algorithms. Games are often used to benchmark reinforcement learning algorithms as they provide a flexible, reproducible, and easy to control environment. Regardless, few games feature a state-space where results in exploration, memory, and planning are easily perceived. This paper presents The Dreaming Variational Autoencoder (DVAE), a neural network based generative modeling architecture for exploration in environments with sparse feedback. We further present Deep Maze, a novel and flexible maze engine that challenges DVAE in partial and fully-observable state-spaces, long-horizon tasks, and deterministic and stochastic problems. We show initial findings and encourage further work in reinforcement learning driven by generative exploration