Modeling cultural accumulation in artificial reinforcement learning agents

Cultural accumulation – the ability to acquire skills and accumulate knowledge over generations – is thought to be a key driver of human success. However, current methodologies for artificial learning systems, such as deep reinforcement learning (RL), typically view the learning problem as occurring over a single “lifetime”. This approach fails to capture the generational and open-ended nature of cultural accumulation observed in humans and other species. Achieving effective cultural accumulation in artificial agents presents significant challenges, including balancing social learning from other agents with independent exploration and discovery, and operating across multiple time scales that govern the acquisition of knowledge, skills, and technological advances.

Previous research has explored different approaches to social learning and cultural accumulation. Expert dropout methods selectively increase the proportion of episodes without demonstrators over time. Bayesian reinforcement learning with constrained intergenerational communication uses domain-specific languages ​​to model social learning in human populations. Large-scale language models, where language serves as the intergenerational communication medium, have also been employed. Although these techniques are promising, they rely on explicit communication channels, incremental adjustments, or domain-specific representations, limiting their applicability. A more general approach that can facilitate knowledge transfer without such constraints is needed.

The researchers propose a robust approach that balances social learning from other agents and independent exploration, enabling cultural accumulation in artificial reinforcement learning agents. They build two different models to explore this accumulation under different notions of generation: episodic generation for in-context learning (knowledge accumulation) and training-time generation for in-weight learning (skill accumulation). By properly balancing these two mechanisms, agents can continuously accumulate knowledge and skills over multiple generations, outperforming agents trained over one lifetime with the same cumulative experience. This work is the first general model for realizing emergent cultural accumulation in reinforcement learning, paving the way for more open-ended learning systems and providing new opportunities to model human cultural evolution.

The researchers propose two different models to investigate the cultural accumulation of agents: in-context accumulation and in-weight accumulation. For in-context accumulation, a meta-reinforcement learning process generates a fixed-policy network with parameters θ. Cultural accumulation occurs during online adaptation to new environments by using the agent's internal state ϕ to distinguish between generations. The length of an episode, T, represents one generation. For in-weight accumulation, each successive generation is trained from randomly initialized parameters θ, with the network weights serving as the basis for accumulation. The number of environment steps, T, used to train each generation represents one generation.

To assess cultural accumulation, the researchers introduce three environments: goal sequence, traveling salesman problem (TSP), and memory sequence. These environments are designed to mimic the process of cultural accumulation seen in humans, requiring agents to discover and transmit information across generations.

Results demonstrate the effectiveness of the proposed cultural accumulation model over single-lifetime reinforcement learning baselines across multiple environments.

In Memory Sequencing EnvironmentIn-context learners trained with the cultural accumulation algorithm outperformed a single-lifetime RL2 baseline and even outperformed a trained noisy oracle when evaluated on novel sequences. Interestingly, accumulation performance deteriorated when the oracle was too accurate, suggesting an over-reliance on social learning that hinders independent in-context learning. Goal Sequence Environment,In-context accumulation significantly outperformed single-lifetime RL2 when,evaluated on novel target sequences.,Higher but imperfect oracle accuracy during training produced the most,effective accumulating agent, due to the difficulty of learning to,follow demonstrations in this partially observable navigation task. TSPSCultural accumulation enabled sustained improvement over RL2 in a single successive context: the routes taken by agents were optimized with each generation, with later generations reducing utilization of a subset of edges.

Overall, the contributions of this study are:

• We propose two models of cultural accumulation in reinforcement learning.
  • In-context models operating on episodic time scales
  • A weighting model that operates across training runs
• Define successful cultural accumulation as a generational process that exceeds independent learning performance with the same experience budget.
• We present an algorithm for a cultural accumulation model that takes into account context and weights.
• Key findings:
  • Accumulation within a context can be hindered by oracles that are too reliable or too unreliable, necessitating a balance between social learning and independent discovery.
  • Weight accumulation effectively mitigates primacy bias
  • Network reset further improves weight accumulation performance.

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