Thought Cloning: Learning to Think while Acting by Imitating Human Thinking (2306.00323v3)

Abstract: Language is often considered a key aspect of human thinking, providing us with exceptional abilities to generalize, explore, plan, replan, and adapt to new situations. However, Reinforcement Learning (RL) agents are far from human-level performance in any of these abilities. We hypothesize one reason for such cognitive deficiencies is that they lack the benefits of thinking in language and that we can improve AI agents by training them to think like humans do. We introduce a novel Imitation Learning framework, Thought Cloning, where the idea is to not just clone the behaviors of human demonstrators, but also the thoughts humans have as they perform these behaviors. While we expect Thought Cloning to truly shine at scale on internet-sized datasets of humans thinking out loud while acting (e.g. online videos with transcripts), here we conduct experiments in a domain where the thinking and action data are synthetically generated. Results reveal that Thought Cloning learns much faster than Behavioral Cloning and its performance advantage grows the further out of distribution test tasks are, highlighting its ability to better handle novel situations. Thought Cloning also provides important benefits for AI Safety and Interpretability, and makes it easier to debug and improve AI. Because we can observe the agent's thoughts, we can (1) more easily diagnose why things are going wrong, making it easier to fix the problem, (2) steer the agent by correcting its thinking, or (3) prevent it from doing unsafe things it plans to do. Overall, by training agents how to think as well as behave, Thought Cloning creates safer, more powerful agents.

• The paper introduces Thought Cloning, a framework that integrates human-like natural language thought processes with action generation, surpassing traditional behavioral cloning.
• It employs dual modules—a Thought Generator and an Action Generator—to improve RL agent planning and adaptation on complex tasks like those in BabyAI.
• Experimental results demonstrate that Thought Cloning significantly boosts out-of-distribution learning and provides new avenues for AI safety and interpretability.

Overview of "Thought Cloning: Learning to Think while Acting by Imitating Human Thinking"

The paper introduces a novel imitation learning framework, Thought Cloning (TC), which aims to enhance the capabilities of reinforcement learning (RL) agents by training them to think in natural language, thereby imitating human thought processes alongside human actions. This innovative framework presents an approach distinct from traditional Behavioral Cloning (BC) methods by leveraging both action and thought data, thereby teaching agents not only to replicate human behavior but also to emulate human cognitive processes during task execution.

Methodology and Framework

Thought Cloning involves a dual-component architecture: a Thought Generator and an Action Generator. At each timestep, the agent receives an observation and mission, along with a history of thoughts. The Thought Generator is responsible for generating thoughts based on these inputs, while the Action Generator determines actions conditioned on the generated thoughts. By integrating natural language into the agent's thinking, Thought Cloning is posited to confer several cognitive advantages such as improved planning, replanning, and generalization to novel situations.

The framework's efficacy is demonstrated through experiments conducted in the BabyAI benchmark, an environment designed to challenge RL agents with tasks requiring high-level planning and execution. Key to the TC framework is the use of a synthetic dataset of "thinking aloud" human demonstrations, produced by translating the internal states of an Oracle Solver into natural language thoughts. This approach allows for the creation of thought datasets that align closely with action data, providing a robust basis for imitation learning.

Experimental Results

The experiments reveal that TC outperforms traditional BC in terms of learning efficiency and performance, particularly in tasks that are out-of-distribution. TC agents demonstrate superior capabilities in adapting to new situations, indicating enhanced generalization potential—a crucial aspect for the development of competent AI systems. The methodology also shows promising applications in the field of AI safety and interpretability, where the transparency of agent thinking allows for mechanisms like Precrime Intervention. This approach halts the execution of potentially unsafe actions by monitoring the thoughts generated by the agent, thereby preventing undesirable outcomes before they occur.

Implications and Future Directions

The implications of Thought Cloning are significant both practically and theoretically. On a practical level, the combination of actions and thoughts in a seamless learning framework offers a path toward the development of more adaptable, interpretable, and safe AI systems. The framework suggests that scaling up the approach with internet-sized datasets of human thought, such as those available through platforms like YouTube, could substantially improve the high-level cognitive abilities of AI agents.

Theoretically, this work challenges the existing paradigms of RL by highlighting the importance of cognitive processes that have been underexplored in AI research. By shifting focus from mere action replication to the inclusion of human-like thought processes, Thought Cloning proposes a more holistic approach to understanding and emulating human intelligence.

Conclusion

Thought Cloning represents a promising avenue for advancing the capabilities of AI agents, emphasizing the integration of natural language in the cognitive framework of RL. It aligns with ongoing efforts to create safer and more interpretable AI systems by offering a method to directly observe and manipulate agent thought processes. Future research may focus on scaling this approach to broader datasets, integrating pre-trained models, and further exploring the alignment of AI behavior with human thoughts and actions. This paper marks a significant step toward redefining how machines can learn to think and act, making them more aligned with human cognition.