Inverse reinforcement learning (IRL) is the problem of inferring the reward function of an
agent, given its policy or observed behavior. Analogous to RL, IRL is perceived both as a …

Dexterous manipulation of the robot is an important part of realizing intelligence, but
manipulators can only perform simple tasks such as sorting and packing in a structured …

In order to drive safely in a dynamic environment, autonomous vehicles should be able to
predict the future states of traffic participants nearby, especially surrounding vehicles, similar …

Deep reinforcement learning (DRL) integrates the feature representation ability of deep
learning with the decision-making ability of reinforcement learning so that it can achieve …

Deep reinforcement learning (RL) has become one of the most popular topics in artificial
intelligence research. It has been widely used in various fields, such as end-to-end control …

Learning from demonstration, or imitation learning, is the process of learning to act in an
environment from examples provided by a teacher. Inverse reinforcement learning (IRL) is a …

Reinforcement learning algorithms describe how an agent can learn an optimal action policy
in a sequential decision process, through repeated experience. In a given environment, the …