Abstract We introduce Imagination-Augmented Agents (I2As), a novel architecture for deep
reinforcement learning combining model-free and model-based aspects. In contrast to most …

In the context of reinforcement learning (RL), in order to increase trust in or understand the
failings of an agent's policy, we propose predictive explanations in the form of three …

We introduce Imagination-Augmented Agents (I2As), a novel architecture for deep
reinforcement learning combining model-free and model-based aspects. In contrast to most …

In recent years, reinforcement learning (RL) systems have shown impressive performance
and remarkable achievements. Many achievements can be attributed to combining RL with …

For an explanation of a deep learning model to be effective, it must provide both insight into
a model and suggest a corresponding action in order to achieve some objective. Too often …

Explainable reinforcement learning methods can roughly be divided into local explanations
that analyze specific decisions of the agents and global explanations that convey the …

Although deep reinforcement learning has become a promising machine learning approach
for sequential decision-making problems, it is still not mature enough for high-stake domains …

Generating explanations for reinforcement learning (RL) is challenging as actions may
produce long-term effects on the future. In this paper, we develop a novel framework for …

Generating counterfactuals to discover hypothetical predictive scenarios is the de facto
standard for explaining machine learning models and their predictions. However, building a …

In order for humans to confidently decide where to employ RL agents for real-world tasks, a
human developer must validate that the agent will perform well at test-time. Some policy …