Modeling Cultural Accumulation in Artificial Reinforcement Learning Agents
What are the main limitations of current artificial learning systems like deep reinforcement learning in terms of cultural accumulation?
The main limitations of current artificial learning systems like deep reinforcement learning in terms of cultural accumulation are as follows:
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Single-lifetime learning: Current methodologies in artificial learning systems, such as deep reinforcement learning (RL), typically frame 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.
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Balancing social learning and independent exploration: Achieving effective cultural accumulation in artificial agents poses significant challenges, including balancing social learning from other agents with independent exploration and discovery.
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Operating over multiple timescales: Cultural accumulation requires operating over multiple timescales that govern the acquisition of knowledge, skills, and technological advances. Current systems struggle to effectively accumulate knowledge and skills over multiple generations.
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Explicit communication channels and domain-specific representations: Previous works have explored various approaches to social learning and cultural accumulation, but these techniques rely on explicit communication channels, incremental adjustments, or domain-specific representations, limiting their broader applicability.
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Primacy bias: In-weights accumulation, a model for cultural accumulation in reinforcement learning, effectively mitigates primacy bias but still faces challenges in achieving sustained improvements across generations.
These limitations highlight the need for more general approaches that can facilitate knowledge transfer without constraints and enable cultural accumulation in artificial reinforcement learning agents.
What are the two distinct models introduced for investigating cultural accumulation in reinforcement learning agents?
The two distinct models introduced for investigating cultural accumulation in reinforcement learning agents are in-context accumulation and in-weights accumulation.
In-context accumulation involves a meta-reinforcement learning process that produces a fixed policy network with parameters θ. Cultural accumulation occurs during online adaptation to new environments by distinguishing between generations using the agent's internal state ϕ. The length of an episode T represents a single generation.
On the other hand, in-weights accumulation involves each successive generation being trained from randomly initialized parameters θ, with the network weights serving as the substrate for accumulation. The number of environment steps T used for training each generation represents a single generation.
How do the proposed models by researchers address the challenges of balancing social learning and independent exploration in artificial agents?
The researchers propose a robust approach that balances social learning from other agents with independent exploration, enabling cultural accumulation in artificial reinforcement learning agents. They construct two distinct models to explore this accumulation under different notions of generations: episodic generations for in-context learning (knowledge accumulation) and train-time generations for in-weights learning (skill accumulation). By striking the right balance between these two mechanisms, the agents can continuously accumulate knowledge and skills over multiple generations, outperforming agents trained for a single lifetime with the same cumulative experience. This work represents the first general models to achieve emergent cultural accumulation in reinforcement learning, paving the way for more open-ended learning systems and presenting new opportunities for modeling human cultural evolution.