TD-MPC2: Scalable, Robust World Models for Continuous Control
Authors: name: Nicklas Hansen, name: Hao Su, name: Xiaolong Wang

TD-MPC is a model-based reinforcement learning (RL) algorithm that performs
local trajectory optimization in the latent space of a learned implicit
(decoder-free) world model. In this work, we present TD-MPC2: a series of
improvements upon the TD-MPC algorithm. We demonstrate that TD-MPC2 improves
significantly over baselines across 104 online RL tasks spanning 4 diverse task
domains, achieving consistently strong results with a single set of
hyperparameters. We further show that agent capabilities increase with model
and data size, and successfully train a single 317M parameter agent to perform
80 tasks across multiple task domains, embodiments, and action spaces. We
conclude with an account of lessons, opportunities, and risks associated with
large TD-MPC2 agents. Explore videos, models, data, code, and more at
https://nicklashansen.github.io/td-mpc2