Artificial Generational Intelligence: Cultural Accumulation in Reinforcement Learning (2406.00392v2)

Abstract: Cultural accumulation drives the open-ended and diverse progress in capabilities spanning human history. It builds an expanding body of knowledge and skills by combining individual exploration with inter-generational information transmission. Despite its widespread success among humans, the capacity for artificial learning agents to accumulate culture remains under-explored. In particular, approaches to reinforcement learning typically strive for improvements over only a single lifetime. Generational algorithms that do exist fail to capture the open-ended, emergent nature of cultural accumulation, which allows individuals to trade-off innovation and imitation. Building on the previously demonstrated ability for reinforcement learning agents to perform social learning, we find that training setups which balance this with independent learning give rise to cultural accumulation. These accumulating agents outperform those trained for a single lifetime with the same cumulative experience. We explore this accumulation by constructing two models under two distinct notions of a generation: episodic generations, in which accumulation occurs via in-context learning and train-time generations, in which accumulation occurs via in-weights learning. In-context and in-weights cultural accumulation can be interpreted as analogous to knowledge and skill accumulation, respectively. To the best of our knowledge, this work is the first to present general models that achieve emergent cultural accumulation in reinforcement learning, opening up new avenues towards more open-ended learning systems, as well as presenting new opportunities for modelling human culture.

• The paper introduces cultural accumulation mechanisms in RL, enabling agents to build upon prior generations’ experiences.
• It compares two models—in-context and in-weights accumulation—and reports superior performance on memory, navigation, and TSP tasks.
• The study highlights implications for robust AI applications and opens new avenues for adaptive, generational learning systems.

Artificial Generational Intelligence: Cultural Accumulation in Reinforcement Learning

The paper "Artificial Generational Intelligence: Cultural Accumulation in Reinforcement Learning" explores the concept of cultural accumulation in reinforcement learning (RL) agents. Cultural accumulation is a mechanism by which knowledge and skills are aggregated and improved upon across generations, a key factor in human societal progress. This research aims to adapt these principles to create more advanced and open-ended learning systems within AI.

Introduction

The authors start by highlighting the significance of cultural accumulation, not only among humans but also in other species. Unlike most RL approaches that optimize agent performance within a single lifetime, this paper investigates the potential of agents to learn from and build upon the experiences of previous generations. The key idea is to balance social learning (learning from others) with independent discovery to achieve continuous generational improvement.

Methods

Two main models of cultural accumulation are introduced: in-context accumulation and in-weights accumulation. Both models are evaluated in the RL context using episodic generations and train-time generations, respectively.

1. In-Context Accumulation: This approach operates within the episodes of an RL task. Agents learn by observing the actions of previous generations while still maintaining the ability to independently explore and adapt. This mixture of social learning and independent adaptation is proposed to produce cumulative knowledge and skills that outperform single-lifetime learning agents.
2. In-Weights Accumulation: Here, cultural accumulation occurs at the training phase by modifying the weights of neural networks across generations. This model is analogous to skill accumulation over longer periods and aims to integrate and improve upon the learning observed in earlier generations.

Experiments and Results

The experiments are conducted in three different environments that test various aspects of partial observability and exploration needs:

• Memory Sequence: A task where agents must memorize and recall sequences of digits.
• Goal Sequence: An adaptation where agents navigate through a grid to reach goals in a specific sequence.
• Travelling Salesperson Problem (TSP): A partial observability variant where agents optimize their paths through a set of cities.

Strong Numerical Results:

• In the Memory Sequence task, agents trained for in-context accumulation significantly outperform both single-lifetime RL baselines and those trained with noisy oracles, demonstrating superior performance even on new sequences.
• In the Goal Sequence and TSP tasks, similar patterns of improved performance across generations are observed, reinforcing the concept that cultural accumulation can lead to enhanced and more efficient learning processes.

Implications and Future Directions

The research demonstrates that reinforcement learning agents can effectively utilize cultural accumulation for superior performance. This has significant implications for both practical applications in AI and theoretical understandings of learning mechanisms.

Practical Implications:

• Enhanced models for RL agents in navigation, optimization, and sequence prediction tasks.
• Potential applications in robotics, game AI, and other areas where planning and continuous learning are crucial.

Theoretical Implications:

• Advances our understanding of how cultural accumulation can be modeled and utilized in artificial systems.
• Opens avenues for more adaptive and resilient learning algorithms that imitate the overarching principles of human societal progress.

Speculative Future Developments

Future studies may explore:

• Curriculum Learning: Using automated methods to determine when agents should rely more heavily on social learning versus independent exploration.
• Complex Environments: Applying cultural accumulation models to more complex and dynamic environments where human-like adaptability is needed.
• Heterogeneous Rewards: Investigating different reward structures and their effects on cultural accumulation.

Overall, this paper underscores the importance of cultural accumulation in building sophisticated AI systems, providing a robust framework for ongoing research and development in reinforcement learning and beyond.