Hierarchical Reinforcement Learning

Reinforcement Learning (RL) research often models environments as Markov decision processes. Yet many real-world tasks are …

We describe a research programme aimed at the construction of a single, generally intelligent agent—one competent across all …

We consider the problem of unsupervised skill segmentation and hierarchical structure discovery in reinforcement learning. While recent …

We consider how to construct state abstractions compatible with a given set of abstract actions, to obtain a well-formed abstract …

We present counting reward automata—a finite state machine variant capable of modelling any reward function expressible as a …

Deep Reinforcement Learning (DRL) has shown breakthroughs in solving challenging problems, such as pixel-based games and continuous …

We present a method for learning options from segmented demonstration trajectories. The trajectories are first segmented into skills …

Hierarchical architectures are critical to the scalability of reinforcement learning methods. Most current hierarchical frameworks …