5 papers
Align and Filter: Improving Performance in Asynchronous On-Policy RL
Homayoun Honari, Roger Creus Castanyer, Michael Przystupa +3
Distributed training and increasing the gradient update frequency are practical strategies to accelerate learning and improve performance, but both exacerbate a central challenge:…
Context Representation via Action-Free Transformer encoder-decoder for Meta Reinforcement Learning
Amir M. Soufi Enayati, Homayoun Honari, Homayoun Najjaran
Reinforcement learning (RL) enables robots to operate in uncertain environments, but standard approaches often struggle with poor generalization to unseen tasks. Context-adaptive m…
A Cross-Environment and Cross-Embodiment Path Planning Framework via a Conditional Diffusion Model
Mehran Ghafarian Tamizi, Homayoun Honari, Amir Mehdi Soufi Enayati +2
Path planning for a robotic system in high-dimensional cluttered environments needs to be efficient, safe, and adaptable for different environments and hardware. Conventional metho…
Meta SAC-Lag: Towards Deployable Safe Reinforcement Learning via MetaGradient-based Hyperparameter Tuning
Homayoun Honari, Amir Mehdi Soufi Enayati, Mehran Ghafarian Tamizi +1
Safe Reinforcement Learning (Safe RL) is one of the prevalently studied subcategories of trial-and-error-based methods with the intention to be deployed on real-world systems. In s…
Safety Optimized Reinforcement Learning via Multi-Objective Policy Optimization
Homayoun Honari, Mehran Ghafarian Tamizi, Homayoun Najjaran
Safe reinforcement learning (Safe RL) refers to a class of techniques that aim to prevent RL algorithms from violating constraints in the process of decision-making and exploration…