Search Results for author:
Found 13 papers, 6 papers with code
Unlocking the Potential of Transformers in Time Series Forecasting with Sharpness-Aware Minimization and Channel-Wise Attention
Transformer-based architectures achieved breakthrough performance in natural language processing and computer vision, yet they remain inferior to simpler linear baselines in multivariate long-term forecasting.
A call for embodied AI
We propose Embodied AI as the next fundamental step in the pursuit of Artificial General Intelligence, juxtaposing it against current AI advancements, particularly Large Language Models.
A Multi-step Loss Function for Robust Learning of the Dynamics in Model-based Reinforcement Learning
In model-based reinforcement learning, most algorithms rely on simulating trajectories from one-step models of the dynamics learned on data.
Multi-timestep models for Model-based Reinforcement Learning
In model-based reinforcement learning (MBRL), most algorithms rely on simulating trajectories from one-step dynamics models learned on data.
Model-based Reinforcement Learning
reinforcement-learning
+1
Guided Safe Shooting: model based reinforcement learning with safety constraints
In the last decade, reinforcement learning successfully solved complex control tasks and decision-making problems, like the Go board game.
Learning in Sparse Rewards settings through Quality-Diversity algorithms
In this thesis, we approach the problem of sparse rewards with these algorithms, and in particular with Novelty Search (NS).
Discovering and Exploiting Sparse Rewards in a Learned Behavior Space
Learning optimal policies in sparse rewards settings is difficult as the learning agent has little to no feedback on the quality of its actions.
Sparse Reward Exploration via Novelty Search and Emitters
Contrary to existing emitters-based approaches, SERENE separates the search space exploration and reward exploitation into two alternating processes.
Novelty Search makes Evolvability Inevitable
Evolvability is thus a natural byproduct of the search in this context.
Unsupervised Learning and Exploration of Reachable Outcome Space
Results show that TAXONS can find a diverse set of controllers, covering a good part of the ground-truth outcome space, while having no information about such space.
Towards continuous control of flippers for a multi-terrain robot using deep reinforcement learning
In this paper we focus on developing a control algorithm for multi-terrain tracked robots with flippers using a reinforcement learning (RL) approach.
A Data-driven Model for Interaction-aware Pedestrian Motion Prediction in Object Cluttered Environments
This paper reports on a data-driven, interaction-aware motion prediction approach for pedestrians in environments cluttered with static obstacles.
Robotics
Virtual-to-real Deep Reinforcement Learning: Continuous Control of Mobile Robots for Mapless Navigation
We present a learning-based mapless motion planner by taking the sparse 10-dimensional range findings and the target position with respect to the mobile robot coordinate frame as input and the continuous steering commands as output.
