In the domain of reinforcement learning (RL), deriving efficacious state representations and
maintaining algorithmic stability are crucial for optimal agent performance. However, the …

A good state representation is crucial to reinforcement learning (RL) while an ideal
representation is hard to learn only with signals from the RL objective. Thus, many recent …

Reinforcement learning (RL) algorithms face the challenge of limited data efficiency,
particularly when dealing with high-dimensional state spaces and large-scale problems …

Compared to reinforcement learning, imitation learning (IL) is a powerful paradigm for
training agents to learn control policies efficiently from expert demonstrations. However, in …

Generative Adversarial Imitation Learning (GAIL) is a powerful and practical approach for
learning sequential decision-making policies. Different from Reinforcement Learning (RL) …

Reinforcement learning (RL) can potentially be applied to real-world robot control in
complex and uncertain environments. However, it is difficult or even unpractical to design an …

Imitation learning aims to solve the problem of defining reward functions in real-world
decision-making tasks. The current popular approach is the Adversarial Imitation Learning …

Recently, various auxiliary tasks have been proposed to accelerate representation learning
and improve sample efficiency in deep reinforcement learning (RL). However, existing …

A good state representation is crucial to solving complicated reinforcement learning (RL)
challenges. Many recent works focus on designing auxiliary losses for learning informative …

Reinforcement learning (RL) algorithms face the challenge of limited data efficiency,
particularly when dealing with high-dimensional state spaces and large-scale problems …