Search Results for author:
Found 7 papers, 2 papers with code
Overcoming Knowledge Barriers: Online Imitation Learning from Observation with Pretrained World Models
In this paper, we study Imitation Learning from Observation with pretrained models and find existing approaches such as BCO and AIME face knowledge barriers, specifically the Embodiment Knowledge Barrier (EKB) and the Demonstration Knowledge Barrier (DKB), greatly limiting their performance.
Action Inference by Maximising Evidence: Zero-Shot Imitation from Observation with World Models
Our method is "zero-shot" in the sense that it does not require further training for the world model or online interactions with the environment after given the demonstration.
Exploration via Empowerment Gain: Combining Novelty, Surprise and Learning Progress
We show that while such an agent is still novelty seeking, i. e. interested in exploring the whole state space, it focuses on exploration where its perceived influence is greater, avoiding areas of greater stochasticity or traps that limit its control.
Learning to Fly via Deep Model-Based Reinforcement Learning
Learning to control robots without requiring engineered models has been a long-term goal, promising diverse and novel applications.
Model-based Reinforcement Learning
reinforcement-learning
+1
Beta DVBF: Learning State-Space Models for Control from High Dimensional Observations
Learning a model of dynamics from high-dimensional images can be a core ingredient for success in many applications across different domains, especially in sequential decision making.
Switching Linear Dynamics for Variational Bayes Filtering
System identification of complex and nonlinear systems is a central problem for model predictive control and model-based reinforcement learning.
Unsupervised Real-Time Control through Variational Empowerment
We introduce a methodology for efficiently computing a lower bound to empowerment, allowing it to be used as an unsupervised cost function for policy learning in real-time control.
