ViSaRL: Visual Reinforcement Learning Guided by Human Saliency

no code implementations • 16 Mar 2024 • Anthony Liang, Jesse Thomason, Erdem Biyik

Using ViSaRL to learn visual representations significantly improves the success rate, sample efficiency, and generalization of an RL agent on diverse tasks including DeepMind Control benchmark, robot manipulation in simulation and on a real robot.

reinforcement-learning Reinforcement Learning (RL) +1

DynaMITE-RL: A Dynamic Model for Improved Temporal Meta-Reinforcement Learning

no code implementations • 25 Feb 2024 • Anthony Liang, Guy Tennenholtz, Chih-Wei Hsu, Yinlam Chow, Erdem Biyik, Craig Boutilier

We introduce DynaMITE-RL, a meta-reinforcement learning (meta-RL) approach to approximate inference in environments where the latent state evolves at varying rates.

Continuous Control Meta Reinforcement Learning

Reinforcement Learning for Sparse-Reward Object-Interaction Tasks in a First-person Simulated 3D Environment

no code implementations • 28 Oct 2020 • Wilka Carvalho, Anthony Liang, Kimin Lee, Sungryull Sohn, Honglak Lee, Richard L. Lewis, Satinder Singh

In this work, we show that one can learn object-interaction tasks from scratch without supervision by learning an attentive object-model as an auxiliary task during task learning with an object-centric relational RL agent.

Object Reinforcement Learning (RL) +1