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 …
Why generalization in rl is difficult: Epistemic pomdps and implicit partial observability
Generalization is a central challenge for the deployment of reinforcement learning (RL)
systems in the real world. In this paper, we show that the sequential structure of the RL …
systems in the real world. In this paper, we show that the sequential structure of the RL …
Offline rl policies should be trained to be adaptive
Offline RL algorithms must account for the fact that the dataset they are provided may leave
many facets of the environment unknown. The most common way to approach this challenge …
many facets of the environment unknown. The most common way to approach this challenge …
Hybrid hierarchical learning for solving complex sequential tasks using the robotic manipulation network ROMAN
Solving long sequential tasks remains a non-trivial challenge in the field of embodied
artificial intelligence. Enabling a robotic system to perform diverse sequential tasks with a …
artificial intelligence. Enabling a robotic system to perform diverse sequential tasks with a …
Deep generative models for offline policy learning: Tutorial, survey, and perspectives on future directions
Deep generative models (DGMs) have demonstrated great success across various domains,
particularly in generating texts, images, and videos using models trained from offline data …
particularly in generating texts, images, and videos using models trained from offline data …
Variational inference mpc for bayesian model-based reinforcement learning
M Okada, T Taniguchi - Conference on robot learning, 2020 - proceedings.mlr.press
In recent studies on model-based reinforcement learning (MBRL), incorporating uncertainty
in forward dynamics is a state-of-the-art strategy to enhance learning performance, making …
in forward dynamics is a state-of-the-art strategy to enhance learning performance, making …
Inverse decision modeling: Learning interpretable representations of behavior
Decision analysis deals with modeling and enhancing decision processes. A principal
challenge in improving behavior is in obtaining a transparent* description* of existing …
challenge in improving behavior is in obtaining a transparent* description* of existing …
Strictly batch imitation learning by energy-based distribution matching
Consider learning a policy purely on the basis of demonstrated behavior---that is, with no
access to reinforcement signals, no knowledge of transition dynamics, and no further …
access to reinforcement signals, no knowledge of transition dynamics, and no further …
Inverse constrained reinforcement learning
In real world settings, numerous constraints are present which are hard to specify
mathematically. However, for the real world deployment of reinforcement learning (RL), it is …
mathematically. However, for the real world deployment of reinforcement learning (RL), it is …