An algorithmic perspective on imitation learning
As robots and other intelligent agents move from simple environments and problems to more
complex, unstructured settings, manually programming their behavior has become …
complex, unstructured settings, manually programming their behavior has become …
A survey of inverse reinforcement learning
Learning from demonstration, or imitation learning, is the process of learning to act in an
environment from examples provided by a teacher. Inverse reinforcement learning (IRL) is a …
environment from examples provided by a teacher. Inverse reinforcement learning (IRL) is a …
Skill induction and planning with latent language
We present a framework for learning hierarchical policies from demonstrations, using sparse
natural language annotations to guide the discovery of reusable skills for autonomous …
natural language annotations to guide the discovery of reusable skills for autonomous …
Inverse reinforcement learning as the algorithmic basis for theory of mind: current methods and open problems
J Ruiz-Serra, MS Harré - Algorithms, 2023 - mdpi.com
Theory of mind (ToM) is the psychological construct by which we model another's internal
mental states. Through ToM, we adjust our own behaviour to best suit a social context, and …
mental states. Through ToM, we adjust our own behaviour to best suit a social context, and …
Maximum-likelihood inverse reinforcement learning with finite-time guarantees
Inverse reinforcement learning (IRL) aims to recover the reward function and the associated
optimal policy that best fits observed sequences of states and actions implemented by an …
optimal policy that best fits observed sequences of states and actions implemented by an …
Learning robot skills with temporal variational inference
In this paper, we address the discovery of robotic options from demonstrations in an
unsupervised manner. Specifically, we present a framework to jointly learn low-level control …
unsupervised manner. Specifically, we present a framework to jointly learn low-level control …
Imitation learning-based implicit semantic-aware communication networks: Multi-layer representation and collaborative reasoning
Semantic communication has recently attracted significant interest from both industry and
academia due to its potential to transform the existing data-focused communication …
academia due to its potential to transform the existing data-focused communication …
Unifying pairwise interactions in complex dynamics
Scientists have developed hundreds of techniques to measure the interactions between
pairs of processes in complex systems, but these computational methods—from …
pairs of processes in complex systems, but these computational methods—from …
Infinite time horizon maximum causal entropy inverse reinforcement learning
M Bloem, N Bambos - 53rd IEEE conference on decision and …, 2014 - ieeexplore.ieee.org
We extend the maximum causal entropy framework for inverse reinforcement learning to the
infinite time horizon discounted reward setting. To do so, we maximize discounted future …
infinite time horizon discounted reward setting. To do so, we maximize discounted future …
Inverse reinforcement learning from failure
Inverse reinforcement learning (IRL) allows autonomous agents to learn to solve complex
tasks from successful demonstrations. However, in many settings, eg, when a human learns …
tasks from successful demonstrations. However, in many settings, eg, when a human learns …