Deep reinforcement learning has achieved great success in many fields and has shown
promise in learning robust skills for robot control in recent years. However, sampling …

Industry 4.0, as an enabler of smart factories, focuses on flexible automation and
customization of products by utilizing technologies such as the Internet of Things and cyber …

Learning to combine control at the level of joint torques with longer-term goal-directed
behavior is a long-standing challenge for physically embodied artificial agents. Intelligent …

Social navigation is the capability of an autonomous agent, such as a robot, to navigate in a
“socially compliant” manner in the presence of other intelligent agents such as humans. With …

Numerous methods are proposed for the Traffic Signal Control (TSC) tasks aiming to provide
efficient transportation and mitigate congestion waste. In recent, promising results have …

Understanding the gap between simulation and reality is critical for reinforcement learning
with legged robots, which are largely trained in simulation. However, recent work has …

This study evaluates the application of a discrete action space reinforcement learning
method (Q-learning) to the continuous control problem of robot inverted pendulum …

Robot control policies learned in simulation do not often transfer well to the real world. Many
existing solutions to this sim-to-real problem, such as the Grounded Action Transformation …

Traffic signal control (TSC) is a complex and important task that affects the daily lives of
millions of people. Reinforcement Learning (RL) has shown promising results in optimizing …

The ability to transfer a policy from one environment to another is a promising avenue for
efficient robot learning in realistic settings where task supervision is not available. This can …