3 citations · 4 across the 8 of their papers we have counts for
5 papers · 1 filter
Online Adaptation for Enhancing Imitation Learning Policies
Federico Malato, Ville Hautamaki
Imitation learning enables autonomous agents to learn from human examples, without the need for a reward signal. Still, if the provided dataset does not encapsulate the task correc…
Zero-shot Imitation Policy via Search in Demonstration Dataset
Federco Malato, Florian Leopold, Andrew Melnik +1
Behavioral cloning uses a dataset of demonstrations to learn a policy. To overcome computationally expensive training procedures and address the policy adaptation problem, we propo…
Behavioral Cloning via Search in Embedded Demonstration Dataset
Federico Malato, Florian Leopold, Ville Hautamaki +1
Behavioural cloning uses a dataset of demonstrations to learn a behavioural policy. To overcome various learning and policy adaptation problems, we propose to use latent space to i…
Towards Solving Fuzzy Tasks with Human Feedback: A Retrospective of the MineRL BASALT 2022 Competition
Stephanie Milani, Anssi Kanervisto, Karolis Ramanauskas +27
To facilitate research in the direction of fine-tuning foundation models from human feedback, we held the MineRL BASALT Competition on Fine-Tuning from Human Feedback at NeurIPS 20…
Improving Behavioural Cloning with Human-Driven Dynamic Dataset Augmentation
Federico Malato, Joona Jehkonen, Ville Hautamäki
Behavioural cloning has been extensively used to train agents and is recognized as a fast and solid approach to teach general behaviours based on expert trajectories. Such method f…