4 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…