Partial Models for Building Adaptive Model-Based Reinforcement Learning Agents

no code implementations • 27 May 2024 • Safa Alver, Ali Rahimi-Kalahroudi, Doina Precup

We demonstrate this by showing that the use of partial models in agents such as deep Dyna-Q, PlaNet and Dreamer can allow for them to effectively adapt to the local changes in their environments.

Replay Buffer with Local Forgetting for Adapting to Local Environment Changes in Deep Model-Based Reinforcement Learning

no code implementations • 15 Mar 2023 • Ali Rahimi-Kalahroudi, Janarthanan Rajendran, Ida Momennejad, Harm van Seijen, Sarath Chandar

This is challenging for deep-learning-based world models due to catastrophic forgetting.

Model-based Reinforcement Learning reinforcement-learning +1

Towards Evaluating Adaptivity of Model-Based Reinforcement Learning Methods

1 code implementation • 25 Apr 2022 • Yi Wan, Ali Rahimi-Kalahroudi, Janarthanan Rajendran, Ida Momennejad, Sarath Chandar, Harm van Seijen

We empirically validate these insights in the case of linear function approximation by demonstrating that a modified version of linear Dyna achieves effective adaptation to local changes.

Model-based Reinforcement Learning reinforcement-learning +1