The proliferation of car sharing services in recent years presents a promising avenue for
advancing sustainable transportation. Beyond merely reducing car ownership rates, these …

This paper proposes a step toward approximate Bayesian inference in on-policy actor-critic
deep reinforcement learning. It is implemented through three changes to the Asynchronous …

Reinforcement Learning or optimal control can provide effective reasoning for sequential
decision-making problems with variable dynamics. Such reasoning in practical …

Safe offline reinforcement learning aims to learn policies that maximize cumulative rewards
while adhering to safety constraints, using only offline data for training. A key challenge is …

This work addresses inverse dynamic games, which generalize the inverse problem of
optimal control, and where the aim is to identify cost functions based on observed optimal …

Model predictive control (MPC) is a popular approach for trajectory optimization in practical
robotics applications. MPC policies can optimize trajectory parameters under kinodynamic …

In recent years, deep reinforcement learning (RL) and imitation learning (IL) have shown
remarkable success in many robotics areas. However, the domain of in-hand dexterous …

Robots deployed into many real-world scenarios are expected to face situations that their
designers could not anticipate. Machine learning is an effective tool for extending the …

Although model-based and model-free approaches to learning the control of systems have
achieved impressive results on standard benchmarks, generalization to variations in the task …