Multi-objective reinforcement learning (MORL) has been proposed to learn control policies
over multiple competing objectives with each possible preference over returns. However …

Imitation learning from human feedback studies how to train well-performed imitation agents
with an annotator's relative comparison of two demonstrations (one demonstration is …

In sequential decision-making, imitation learning (IL) trains a policy efficiently by mimicking
expert demonstrations. Various imitation methods were proposed and empirically evaluated …

Adversarial imitation learning has become a widely used imitation learning framework. The
discriminator is often trained by taking expert demonstrations and policy trajectories as …

Imitation learning learns a policy from expert trajectories. While the expert data is believed to
be crucial for imitation quality, it was found that a kind of imitation learning approach …

Imitation learning has emerged as a promising approach for addressing sequential decision-
making problems, with the assumption that expert demonstrations are optimal. However, in …

In many real-world imitation learning tasks, the demonstrator and the learner have to act
under different observation spaces. This situation brings significant obstacles to existing …

Reinforcement Learning (RL)[38] has been widely used in many challenging sequential
decision-making tasks, such as autonomous vehicle [11, 21, 34], video game playing [3, 8 …

Adversarial Imitation Learning (AIL) allows the agent to reproduce expert behavior with low-
dimensional states and actions. However, challenges arise in handling visual states due to …

In Reinforcement Learning (RL), it is commonly assumed that an immediate reward signal is
generated for each action taken by the agent, helping the agent maximize cumulative …