Human-Timescale Adaptation in an Open-Ended Task Space (2301.07608v1)

Abstract: Foundation models have shown impressive adaptation and scalability in supervised and self-supervised learning problems, but so far these successes have not fully translated to reinforcement learning (RL). In this work, we demonstrate that training an RL agent at scale leads to a general in-context learning algorithm that can adapt to open-ended novel embodied 3D problems as quickly as humans. In a vast space of held-out environment dynamics, our adaptive agent (AdA) displays on-the-fly hypothesis-driven exploration, efficient exploitation of acquired knowledge, and can successfully be prompted with first-person demonstrations. Adaptation emerges from three ingredients: (1) meta-reinforcement learning across a vast, smooth and diverse task distribution, (2) a policy parameterised as a large-scale attention-based memory architecture, and (3) an effective automated curriculum that prioritises tasks at the frontier of an agent's capabilities. We demonstrate characteristic scaling laws with respect to network size, memory length, and richness of the training task distribution. We believe our results lay the foundation for increasingly general and adaptive RL agents that perform well across ever-larger open-ended domains.

• The paper introduces Adaptive Agent (AdA) that achieves few-shot adaptation across diverse 3D tasks using meta-reinforcement learning and a large-scale attention-based memory architecture.
• The paper employs automated curriculum learning with prioritised level replay to dynamically adjust task difficulty, driving continual skill progression.
• The paper shows that scaling network size, memory, and task diversity significantly boosts RL performance, narrowing the gap between artificial and human learning capabilities.

Overview of "Human-Timescale Adaptation in an Open-Ended Task Space"

The paper examines how reinforcement learning (RL) agents can achieve rapid in-context adaptation comparable to human abilities using a foundation model approach. It highlights a notable gap between the success of foundation models in supervised tasks and their limited impact in reinforcement learning. By training a Reinforcement Learning (RL) agent on a vast set of tasks, the researchers demonstrate that such models can adapt to novel 3D embodied problems quickly, akin to human learning capabilities. The presented Adaptive Agent (AdA) autonomously learns hypothesis-driven exploration and knowledge exploitation, showcasing a potential new paradigm in RL agent training.

Key Contributions and Methodology

1. Adaptive Agent (AdA): The proposed agent can perform few-shot adaptations in a large-scale, 3D, and open-ended task space. This introduces a new aspect of rapid adaptability in agents, traditionally a challenge in reinforcement learning.
2. Meta-Reinforcement Learning: Employed across an extensive range of dynamic and diverse tasks to encourage the emergence of a general in-context learning algorithm.
3. Large-Scale Attention-Based Memory Architecture: AdA utilizes a policy structure based on memory architectures, specifically Transformer models, due to their capabilities of scaling with the number of parameters. Attention mechanisms are crucial for on-the-fly adaptation and hypothesis-driven exploration.
4. Automated Curriculum Learning: A mechanism is introduced to prioritize learning tasks at an agent's capability frontier, preventing stagnation and ensuring continual learning progression. This is achieved using no-op filtering and prioritised level replay (PLR) to manage task difficulty dynamically.
5. Scaling Laws Demonstrated: Empirical studies validate that agent performance scales with network size, memory, and task distribution complexity. This aligns with trends observed in other domains using large-scale models, demonstrating the feasibility of scaling up RL-based foundation models.

Results and Implications

• Human-Timescale Adaptation: AdA's ability to adapt quickly across a diverse task distribution matches that of human test subjects, suggesting progress towards developing more effective RL-based foundation models.
• Effective Prompting with Demonstrations: The capability of AdA to leverage first-person demonstrations provided zero-shot improvement, drawing parallels with successful practices in LLMs, reinforcing the adaptability and transfer learning potential in diverse task environments.
• Scalability: Through extensive experimentation, the research illustrates how scaling different components of the training process (network size, task distribution, and curriculum complexity) directly impacts the agent's performance. Larger models and richer task environments push the frontier of task solvability.

Future Directions

The research opens multiple avenues for advancing AI agents:

• Integrated Cross-Domain Applications: While this research focuses on 3D task environments, the principles could be extended to other domains, including robotics and real-world dynamic scenarios.
• Further Exploration of Curriculum Methods: Investigating more sophisticated methods for generating or selecting tasks can further enhance an agent's ability to adapt efficiently.
• Foundation Model Development in RL: Exploring further the use of large-scale attention-based architectures in RL may yield benefits similar to those observed in supervised and semi-supervised learning.

This paper bridges a significant gap between reinforcement learning and adaptability akin to human intelligence, providing concrete steps towards robust, general, and fast-adapting RL agents. It embarks on charting new territories where agents can not only learn efficiently but also generalize rapidly to unseen scenarios, bolstering their real-world applicability.