Deep learning (DL) and reinforcement learning (RL) methods seem to be a part of
indispensable factors to achieve human-level or super-human AI systems. On the other …

Two main challenges in Reinforcement Learning (RL) are designing appropriate reward
functions and ensuring the safety of the learned policy. To address these challenges, we …

This paper considers the problem of recovering the policies of multiple interacting experts by
estimating their reward functions and constraints where the demonstration data of the …

The objective of offline RL is to learn optimal policies when a fixed exploratory
demonstrations data-set is available and sampling additional observations is impossible …

Inverse Reinforcement Learning (IRL) aims to reconstruct the reward function from expert
demonstrations to facilitate policy learning, and has demonstrated its remarkable success in …

Recently, adversarial imitation learning has shown a scalable reward acquisition method for
inverse reinforcement learning (IRL) problems. However, estimated reward signals often …

One of the challenges in applying reinforcement learning in a complex real-world
environment lies in providing the agent with a sufficiently detailed reward function. Any …

Inverse reinforcement learning (IRL) aims to infer a reward from expert demonstrations,
motivated by the idea that the reward, rather than the policy, is the most succinct and …

随着深度强化学习的研究与发展, 强化学习在博弈与优化决策, 智能驾驶等现实问题中的应用也
取得显著进展. 然而强化学习在智能体与环境的交互中存在人工设计奖励函数难的问题 …

The gloabal objective of inverse Reinforcement Learning (IRL) is to estimate the unknown
cost function of some MDP base on observed trajectories generated by (approximate) …