3 citations · 4 across the 8 of their papers we have counts for
9 papers
Zero-shot World Models via Search in Memory
Federico Malato, Ville Hautamäki
World Models have vastly permeated the field of Reinforcement Learning. Their ability to model the transition dynamics of an environment have greatly improved sample efficiency in…
Search-Based Adversarial Estimates for Improving Sample Efficiency in Off-Policy Reinforcement Learning
Federico Malato, Ville Hautamaki
Sample inefficiency is a long-lasting challenge in deep reinforcement learning (DRL). Despite dramatic improvements have been made, the problem is far from being solved and is espe…
ROAR: Reinforcing Original to Augmented Data Ratio Dynamics for Wav2Vec2.0 Based ASR
Vishwanath Pratap Singh, Federico Malato, Ville Hautamaki +2
While automatic speech recognition (ASR) greatly benefits from data augmentation, the augmentation recipes themselves tend to be heuristic. In this paper, we address one of the heu…
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…
Behavioral Cloning via Search in Video PreTraining Latent Space
Federico Malato, Florian Leopold, Amogh Raut +2
Our aim is to build autonomous agents that can solve tasks in environments like Minecraft. To do so, we used an imitation learning-based approach. We formulate our control problem…
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…