Goal misalignment, reward sparsity and difficult credit assignment are only a few of the many
issues that make it difficult for deep reinforcement learning (RL) agents to learn optimal …

Deep reinforcement learning (RL) agents rely on shortcut learning, preventing them from
generalizing to slightly different environments. To address this problem, symbolic method …

Reinforcement learning (RL) has proven to be a powerful tool for training agents that excel
in various games. However, the black-box nature of neural network models often hinders our …

Machine Learning is a promising avenue for combating climate change by developing
control policies that can lead to greater efficiency, reducing cost and carbon footprint …

Properly defining a reward signal to efficiently train a reinforcement learning (RL) agent is a
challenging task. Designing balanced objective functions from which a desired behavior can …

We study the problem of building reasoning agents that are able to generalize in an effective
manner. Towards this goal, we propose an end-to-end approach for building model-based …

Deep reinforcement learning aims to learn deep neural network policies to solve large-scale
decision-making problems. However, approximating policies using deep neural networks …