IQL-TD-MPC: Implicit Q-Learning for Hierarchical Model Predictive Control

June 15, 2023


Model-based reinforcement learning (RL) has shown great promise due to its sample efficiency, but still struggles with long-horizon sparse-reward tasks, especially in offline settings where the agent learns from a fixed dataset. We hypothesize that model-based RL agents struggle in these environments due to a lack of long-term planning capabilities, and that planning in a temporally abstract model of the environment can alleviate this issue. In this paper, we make two key contributions: 1) we introduce an offline model-based RL algorithm, IQL-TD-MPC, that extends the state-of-the-art Temporal Difference Learning for Model Predictive Control (TD-MPC) with Implicit Q-Learning (IQL); 2) we propose to use IQL-TD-MPC as a Manager in a hierarchical setting with any off-the-shelf offline RL algorithm as a Worker. More specifically, we pre-train a temporally abstract IQL-TD-MPC Manager to predict "intent embeddings", which roughly correspond to subgoals, via planning. We empirically show that augmenting state representations with intent embeddings generated by an IQL-TD-MPC manager significantly improves off-the-shelf offline RL agents' performance on some of the most challenging D4RL benchmark tasks. For instance, the offline RL algorithms AWAC, TD3-BC, DT, and CQL all get zero or near-zero normalized evaluation scores on the medium and large antmaze tasks, while our modification gives an average score over 40.

Download the Paper


Written by

Yingchen Xu

Bobak Hashemi

Lucas Lehnert

Rohan Chitnis

Urun Dogan

Zheqing (Bill) Zhu

Olivier Delalleau



Research Topics

Reinforcement Learning

Related Publications

July 01, 2024


Behaviour Distillation

Andrei Lupu, Chris Lu, Robert Lange, Jakob Foerster

July 01, 2024

May 06, 2024



Solving General Noisy Inverse Problem via Posterior Sampling: A Policy Gradient Viewpoint

Haoyue Tang, Tian Xie

May 06, 2024

April 30, 2024


Multi-Agent Diagnostics for Robustness via Illuminated Diversity

Mikayel Samvelyan, Minqi Jiang, Davide Paglieri, Jack Parker-Holder, Tim Rocktäschel

April 30, 2024

April 02, 2024



MoDem-V2: Visuo-Motor World Models for Real-World Robot Manipulation

Patrick Lancaster, Nicklas Hansen, Aravind Rajeswaran, Vikash Kumar

April 02, 2024

Help Us Pioneer The Future of AI

We share our open source frameworks, tools, libraries, and models for everything from research exploration to large-scale production deployment.