A survey of imitation learning: Algorithms, recent developments, and challenges
M Zare, PM Kebria, A Khosravi… - IEEE Transactions on …, 2024 - ieeexplore.ieee.org
In recent years, the development of robotics and artificial intelligence (AI) systems has been
nothing short of remarkable. As these systems continue to evolve, they are being utilized in …
nothing short of remarkable. As these systems continue to evolve, they are being utilized in …
Ceil: Generalized contextual imitation learning
In this paper, we present ContExtual Imitation Learning (CEIL), a general and broadly
applicable algorithm for imitation learning (IL). Inspired by the formulation of hindsight …
applicable algorithm for imitation learning (IL). Inspired by the formulation of hindsight …
Discriminator-weighted offline imitation learning from suboptimal demonstrations
We study the problem of offline Imitation Learning (IL) where an agent aims to learn an
optimal expert behavior policy without additional online environment interactions. Instead …
optimal expert behavior policy without additional online environment interactions. Instead …
Benchmarks and algorithms for offline preference-based reward learning
Learning a reward function from human preferences is challenging as it typically requires
having a high-fidelity simulator or using expensive and potentially unsafe actual physical …
having a high-fidelity simulator or using expensive and potentially unsafe actual physical …
Beyond uniform sampling: Offline reinforcement learning with imbalanced datasets
Offline reinforcement learning (RL) enables learning a decision-making policy without
interaction with the environment. This makes it particularly beneficial in situations where …
interaction with the environment. This makes it particularly beneficial in situations where …
Imitation learning from imperfection: Theoretical justifications and algorithms
Imitation learning (IL) algorithms excel in acquiring high-quality policies from expert data for
sequential decision-making tasks. But, their effectiveness is hampered when faced with …
sequential decision-making tasks. But, their effectiveness is hampered when faced with …
Offline Goal-Conditioned Reinforcement Learning via -Advantage Regression
JY Ma, J Yan, D Jayaraman… - Advances in neural …, 2022 - proceedings.neurips.cc
Offline goal-conditioned reinforcement learning (GCRL) promises general-purpose skill
learning in the form of reaching diverse goals from purely offline datasets. We propose …
learning in the form of reaching diverse goals from purely offline datasets. We propose …
State-of-the-art in robot learning for multi-robot collaboration: A comprehensive survey
With the continuous breakthroughs in core technology, the dawn of large-scale integration of
robotic systems into daily human life is on the horizon. Multi-robot systems (MRS) built on …
robotic systems into daily human life is on the horizon. Multi-robot systems (MRS) built on …
Survival instinct in offline reinforcement learning
We present a novel observation about the behavior of offline reinforcement learning (RL)
algorithms: on many benchmark datasets, offline RL can produce well-performing and safe …
algorithms: on many benchmark datasets, offline RL can produce well-performing and safe …
Mahalo: Unifying offline reinforcement learning and imitation learning from observations
We study a new paradigm for sequential decision making, called offline policy learning from
observations (PLfO). Offline PLfO aims to learn policies using datasets with substandard …
observations (PLfO). Offline PLfO aims to learn policies using datasets with substandard …