Researchers at UC San Diego have introduced TD-MPC2, an expansion of the TD-MPC family of model-based RL algorithms, to address challenges faced by generalist embodied agents. TD-MPC2 performs local trajectory optimization in the latent space of a trained implicit world model, exhibits algorithmic robustness, and supports datasets with multiple embodiments and action spaces. It outperforms baseline algorithms in RL tasks and demonstrates scalability and efficacy in handling various difficulties. The agent successfully accomplishes 80 tasks with 317 million parameters, showcasing the versatility of TD-MPC2.
UC San Diego Researchers Present TD-MPC2: Revolutionizing Model-Based Reinforcement Learning Across Diverse Domains
Large Language Models (LLMs) powered by Artificial Intelligence and Machine Learning are driving advancements in various sub-fields of AI. These models, trained on massive datasets, are proving to be versatile in handling language and visual tasks. However, extending LLMs to robotics and achieving a generalist embodied agent that can perform multiple control tasks is still a challenge.
The current approaches face two major obstacles. Firstly, they rely on near-expert trajectories, limiting flexibility to different tasks. Secondly, existing reinforcement learning algorithms are not scalable for large, uncurated datasets and are optimized for single-task learning.
To address these challenges, a team of researchers has introduced TD-MPC2, an expansion of the TD-MPC family of model-based RL algorithms. TD-MPC2 is trained on big, uncurated datasets and does not require hyperparameter adjustment.
Key elements of TD-MPC2:
– Local Trajectory Optimization in Latent Space: TD-MPC2 carries out trajectory optimization in the latent space of a trained implicit world model without the need for a decoder.
– Algorithmic Robustness: The algorithm is designed to be more resilient by revisiting important design decisions.
– Architecture for numerous Embodiments and Action Spaces: The architecture supports datasets with multiple embodiments and action spaces without requiring prior domain expertise.
TD-MPC2 outperforms existing model-based and model-free approaches in a range of continuous control tasks, especially in difficult subsets like pick-and-place and locomotion tasks. It also demonstrates scalability as both the model and data size grow.
Notable characteristics of TD-MPC2:
– Enhanced Performance: TD-MPC2 provides enhancements over baseline algorithms in various RL tasks.
– Consistency with a Single Set of Hyperparameters: TD-MPC2 reliably produces impressive outcomes with a single set of hyperparameters, streamlining the tuning process.
– Scalability: Agent capabilities increase as the model and data size grow, allowing for more complex tasks and adaptability to different situations.
The team trained a single agent with 317 million parameters to accomplish 80 tasks across multiple task domains, embodiments, and action spaces. This showcases the versatility and strength of TD-MPC2.
For more details, check out the paper and project.
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