As a subfield of machine learning, reinforcement learning (RL) aims at optimizing decision
making by using interaction samples of an agent with its environment and the potentially …

When making decisions, people often overlook critical information or are overly swayed by
irrelevant information. A common approach to mitigate these biases is to provide decision …

We present a novel sensor-based learning navigation algorithm to compute a collision-free
trajectory for a robot in dense and dynamic environments with moving obstacles or targets …

Programmatic reinforcement learning (PRL) has been explored for representing policies
through programs as a means to achieve interpretability and generalization. Despite …

We present Multiple Scenario Verifiable Reinforcement Learning via Policy Extraction
(MSVIPER), a new method for policy distillation to decision trees for improved robot …

We present Multiple Scenario Verifiable Reinforcement Learning via Policy Extraction
(MSVIPER), a new method for policy distillation to decision trees for improved robot …

Recent advancements in reinforcement learning (RL) expedited its success across a wide
range of decision-making problems. However, a lack of safety guarantees restricts its use in …

Deep reinforcement learning is a powerful tool for learning complex control tasks; however,
neural networks are notoriously “black boxes” and lack many properties desirable of …

Reinforcement learning (RL) is a general method for agents to learn optimal control policies
through exploration and experience. Due to its generality, RL can generate novel policies …