How Large Language Models (LLMs) are Advancing AI and ML
Large Language Models (LLMs) are continually improving thanks to advancements in Artificial Intelligence (AI) and Machine Learning (ML). LLMs play a crucial role in sub-fields of AI, including Natural Language Processing, Natural Language Understanding, Natural Language Generation, and Computer Vision. These models are trained on massive internet-scale datasets to develop versatile models capable of handling various language and visual tasks. The availability of large datasets and scalable architectures is responsible for their growth. Recently, LLMs have also been extended to robotics.
Challenges in Developing Generalist Embodied Agents
Despite the progress, achieving a generalist embodied agent that can perform multiple control tasks using low-level actions from vast uncurated datasets is still a challenge. Existing approaches face two major obstacles:
1. Assumption of Near-Expert Trajectories: Due to limited data availability, many methods for behavior cloning rely on near-expert trajectories. This limits the flexibility of agents to perform different tasks since they require expert-like demonstrations to learn from.
2. Absence of Scalable Continuous Control Methods: Several scalable continuous control methods struggle to effectively handle large, uncurated datasets. Many existing reinforcement learning (RL) algorithms rely on task-specific hyperparameters and are optimized for single-task learning.
Introducing TD-MPC2: An Expansion of Model-Based RL Algorithms
To overcome these challenges, a team of researchers recently introduced TD-MPC2, an expansion of the TD-MPC (Trajectory Distribution Model Predictive Control) family of RL algorithms. TD-MPC2 leverages big, uncurated datasets covering various task domains, embodiments, and action spaces to build generalist world models. One notable feature is that it doesn’t require hyperparameter adjustment. The key elements of TD-MPC2 are:
1. Local Trajectory Optimization in Latent Space: TD-MPC2 performs local trajectory optimization in the latent space of a trained implicit world model without the need for a decoder.
2. Algorithmic Robustness: The algorithm becomes more resilient by reevaluating important design decisions.
3. Architecture for Multiple Embodiments and Action Spaces: Thoughtfully designed architecture supports datasets with multiple embodiments and action spaces without requiring prior domain expertise.
Impressive Performance and Characteristics
TD-MPC2 outperforms current model-based and model-free approaches when applied to a variety of continuous control tasks, especially in challenging subsets like pick-and-place and locomotion tasks. It consistently produces enhanced outcomes with a single set of hyperparameters, streamlining the tuning process and making it applicable to various jobs. As the model and data sizes grow, agent capabilities scale up, demonstrating its scalability.
The team trained a single agent with 317 million parameters to accomplish 80 tasks across multiple task domains, embodiments, and action spaces, highlighting the versatility and strength of TD-MPC2 in addressing a broad range of difficulties.
To learn more about this research, check out the Paper and Project. All credit goes to the researchers behind this project. Join our ML SubReddit, Facebook Community, Discord Channel, and Email Newsletter for the latest AI research news and cool projects. If you enjoy our work, you’ll love our newsletter. Follow us on Telegram and WhatsApp too.
About the author: Tanya Malhotra is a final-year undergraduate student specializing in Artificial Intelligence and Machine Learning at the University of Petroleum & Energy Studies, Dehradun. She is passionate about data science, critical thinking, and acquiring new skills.