Summary Thought Cloning Learning to Think while Acting. arxiv.org
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One Line
Thought Cloning outperforms Behavioral Cloning in solving out-of-distribution environments and has interpretability benefits, using a synchronized dataset of human thinking and action and employing FiLM for modality fusion to address partial observability.
Key Points
- Thought Cloning is an AI learning framework that trains agents to think like humans and behave like them.
- The framework has an Upper-level Component for thought generation and a Lower-level Component for executing actions.
- TC outperforms Behavioral Cloning (BC) in terms of learning speed and solving out-of-distribution environments, demonstrating planning and replanning abilities.
- The approach involves using datasets of humans thinking out loud while performing tasks, allowing agents to learn high-level thinking.
- The TC model uses an LSTM to embed thought history and a transformer encoder to process both mission and observation inputs.
Summaries
208 word summary
A study compared the performance of Thought Cloning (TC) and Behavioral Cloning (BC) agents on challenging environments, finding that TC outperforms BC in solving out-of-distribution environments and has interpretability benefits. The study defines OOD environments, evaluates agents on them, and presents a metric to evaluate the interpretability of TC agents. TC learned faster than BC, with a higher success rate, using a synchronized dataset of human thinking and action. The framework employs FiLM for modality fusion to address partial observability and can be implemented in complex or large-scale scenarios. TC is an imitation learning framework where agents learn to act and think from demonstrations. The TC agent has two components: the Upper-Level Component generates thoughts and the Lower-Level Component generates actions conditioned on these thoughts. The paper discusses the use of TC agents to diagnose issues and improve performance by observing an agent's thoughts. Gradual decay of teacher-forcing rate during training is adopted to help the agent recover from incorrect thoughts and explore new ideas. The TC agent is a model that uses an LSTM to embed thought history and a transformer encoder to process both mission and observation inputs. The article suggests that thought cloning could lead to advancements in artificial general intelligence, AI safety, and interpretability.
430 word summary
The paper "Thought Cloning Learning to Think while Acting" discusses the use of Thought Cloning (TC) agents to diagnose issues and improve performance by observing an agent's thoughts. Gradual decay of teacher-forcing rate during training is adopted to help the agent recover from incorrect thoughts and explore new ideas. Recent advances in natural language processing and deep reinforcement learning have led to the development of hierarchical latent language models, which can induce skills and plan actions through vision-language models. The TC agent is a model that uses an LSTM to embed thought history and a transformer encoder to process both mission and observation inputs. The TC model is composed of an Upper-level Component that generates thoughts and a Lower-level Component that generates actions. The agent is trained to complete various tasks such as putting a green box next to a purple door, by exploring areas and opening doors. The synthetic human thought dataset is used to evaluate the agent's performance. Thought Cloning has potential applications in collaboration with humans on complex tasks and AI safety. The article discusses various studies related to learning and language, as well as robotics and artificial intelligence. The authors suggest that thought cloning could lead to advancements in artificial general intelligence, AI safety, and interpretability. A study compared the performance of Thought Cloning (TC) and Behavioral Cloning (BC) agents on challenging environments, finding that TC outperforms BC in solving out-of-distribution environments and has interpretability benefits. The study defines OOD environments, evaluates agents on them, and presents a metric to evaluate the interpretability of TC agents. To generate a thought dataset, an Oracle Solver was used to translate internal states into natural language thoughts using predefined rules. During testing, TC learned faster than BC, with a higher success rate. The study focuses on using BabyAI to generate step-by-step solutions for challenging missions, and the Thought Cloning training framework teaches agents to think while acting by using a synchronized dataset of human thinking and action. It employs FiLM for modality fusion to address partial observability and can be implemented in complex or large-scale scenarios. Thought Cloning is an AI learning framework that learns faster than Behavioral Cloning and performs well on internet-scale datasets of humans thinking out loud while acting. It is an imitation learning framework where agents learn to act and think from demonstrations, with the TC agent having two components: the Upper-Level Component generates thoughts and the Lower-Level Component generates actions conditioned on these thoughts. The framework offers significant potential for AI safety and interpretability, where unsafe behavior can be near perfectly stopped before execution.
929 word summary
Thought Cloning is an AI learning framework that trains agents to think like humans and behave like them. It learns faster than Behavioral Cloning and performs well on internet-scale datasets of humans thinking out loud while acting. Language is a key aspect of human thinking that helps us generalize, explore, plan, replan, and adapt to new situations. The proposed method, Thought Cloning, is an imitation learning framework where agents learn to act and think from demonstrations. The TC agent has two components: the Upper-Level Component generates thoughts and the Lower-Level Component generates actions conditioned on these thoughts. The framework offers significant potential for AI safety and interpretability, where unsafe behavior can be near perfectly stopped before execution. The thought data, such as YouTube videos and transcripts, contains millions of hours of people talking out loud while performing tasks, revealing the thinking behind their actions, planning, decisions, and replanning. This thought data is greatly valuable and widely available. The Thought Cloning training framework teaches agents to think while acting by using a synchronized dataset of human thinking and action. The framework has an Upper-level Component for thought generation and a Lower-level Component for executing actions. The model employs FiLM for modality fusion to address partial observability. The framework can be implemented in complex or large-scale scenarios.
The study focuses on using BabyAI, a simulated partially observable 2D gridworld domain, to generate step-by-step solutions for challenging missions. The missions consist of multiple tasks requiring complicated navigation and actions, and are described in natural language. The agent's action space includes left, right, forward, pickup, drop, toggle door (unlock, open, close), and occluded grid cells are assigned an item ID of 0.
The study compares the performance of Thought Cloning (TC) and Behavioral Cloning (BC) in terms of learning speed. During testing, TC and BC agents were tested in 512 environments, and success was defined as completing all specified tasks in the mission. Results showed that TC learned faster than BC, with a higher success rate.
To generate a thought dataset, an Oracle Solver was used to translate internal states into natural language thoughts using predefined rules. The main results were produced using ten A40 GPUs over one week. A study compares the performance of Thought Cloning (TC) and Behavioral Cloning (BC) agents on increasingly difficult environments. TC outperforms BC in solving out-of-distribution environments, demonstrating planning and replanning abilities. The study also highlights the interpretability benefits of TC and supports the hypothesis that learning from human thought boosts an agent's ability to think. The study defines OOD environments and evaluates agents on them, finding that TC agents substantially outperform BC agents. The study also presents a metric to evaluate the interpretability of TC agents and finds that Precrime Intervention effectively eliminates unsafe behaviors. The study concludes that leveraging internet-sized datasets of human thinking can enhance the power of TC agents in high-level thinking. Thought Cloning (TC) is a method that enables AI agents to think while they act, providing interpretability and steerability. TC agents can be customized to prevent unsafe behaviors and show promise in advancing AI safety. The approach involves using datasets of humans thinking out loud while performing tasks, allowing agents to learn high-level thinking. This has benefits such as improved AI capabilities, safety, and interpretability. The use of internet-scale datasets is also highlighted. Thought Cloning has potential applications in collaboration with humans on complex tasks and AI safety. The article discusses various studies related to learning and language, as well as robotics and artificial intelligence. The authors suggest that thought cloning could lead to advancements in artificial general intelligence, AI safety, and interpretability. The article also touches on concerns about AI and its potential risks, as well as the importance of developing zero-shot planners and language models as tools for embodied agents. Recent advances in natural language processing and deep reinforcement learning have led to the development of hierarchical latent language models, which can induce skills and plan actions through vision-language models. The Thought Cloning (TC) agent is a model that uses an LSTM to embed thought history and a transformer encoder to process both mission and observation inputs. The TC model is composed of an Upper-level Component that generates thoughts and a Lower-level Component that generates actions. The TC model is trained using a loss function that includes an entropy term for actions. The agent is trained to complete various tasks, such as putting a green box next to a purple door, by exploring areas and opening doors. The synthetic human thought dataset is used to evaluate the agent's performance. Example trajectories are shown, including thoughts and actions taken by the agent at different time intervals. The agent explored different areas and opened doors to explore them, completing missions such as picking up a blue box and going to a purple door. However, the agent got stuck at one point and had incorrect thoughts, which were fixed. The agent skipped 2748 steps and reached the max step. The paper "Thought Cloning Learning to Think while Acting" discusses using the thoughts of Thought Cloning (TC) agents to diagnose issues and improve performance. Without visibility into the agent's thoughts, it can be difficult to pinpoint underlying problems. Gradual decay of teacher-forcing rate during training is adopted to help the agent recover from incorrect thoughts and explore new ideas. The authors provide an example of diagnosing an agent by observing its thoughts and recommend transitioning to auto-regressive training after an initial phase of teacher-forcing training. An example trajectory of an agent completing a mission by dropping a green box is also included.
2347 word summary
In the paper "Thought Cloning Learning to Think while Acting," the authors discuss observing the thoughts of Thought Cloning (TC) agents during development to diagnose issues and improve performance. They note that without visibility into the agent's thoughts, it can be difficult to pinpoint the underlying problems. Gradual decay of teacher-forcing rate during training is adopted to help the agent recover from incorrect thoughts and explore new ideas. The authors provide an example of diagnosing an agent by observing its thoughts and note that constant teacher-forcing training can lead to nonsensical thoughts and a failure to recover from incorrect thoughts. They recommend transitioning to auto-regressive training after an initial phase of teacher-forcing training. The excerpt also includes an example trajectory of an agent completing a mission by dropping a green box. The agent explored different areas and opened doors to explore them, completing missions such as picking up a blue box and going to a purple door. The teacher-forcing rate gradually decayed as the agent learned to think while acting. However, the agent got stuck at one point and had incorrect thoughts, which were fixed. The agent skipped 2748 steps and reached the max step. The paper discusses a method for training an agent to learn to think while acting, using constant auto-regressive training and teacher-forcing training. The agent is trained to complete various tasks, such as putting a green box next to a purple door, by exploring areas and opening doors. The synthetic human thought dataset is used to evaluate the agent's performance. Hyperparameter settings and learning rate schedules are provided. Example trajectories are shown, including thoughts and actions taken by the agent at different time intervals. This document describes the Thought Cloning (TC) framework, which involves encoding thoughts and actions to train a policy network. The TC model is composed of an Upper-level Component that generates thoughts and a Lower-level Component that generates actions. The observations are encoded using a CNN and Bag-of-Words, while thoughts and missions are encoded with a Transformer encoder. The model is trained using a loss function that includes an entropy term for actions. The TC model is evaluated and replicated using key details provided in the supplementary information. The primary difference between the TC model and the baseline model is the additional embedding of the thought generated by the Upper-Level Component. The Thought Cloning (TC) agent is a model that uses an LSTM to embed thought history and a transformer encoder to process both the mission and observation inputs. The Upper-level Component generates thoughts that are inputted into the Lower-level Component to predict actions. The TC agent includes a natural language-defined mission and an observation. The detailed architecture includes a Thought Generator, Attention Encoder, Multi-head Transformer, and Thought History RNN. The training details are listed in the Supplementary Material section. Recent developments in natural language processing and deep reinforcement learning have enabled the generation and following of natural language instructions for decision making in robotic systems. These advancements have led to the development of hierarchical latent language models, which can induce skills and plan actions through vision-language models. Large language models have also been used to enable open-world multi-task agents through interactive planning. Additionally, research has been conducted on adjusting planning horizons with adaptive subgoal search and training helpful and harmless assistants with reinforcement learning from human feedback. Hierarchical task learning from language instructions with unified semantic representation has been explored, as well as the use of persistent spatial abstraction for hierarchical deep reinforcement learning. Language has also been studied as a representation for high-level natural language instruction execution. Finally, recurrent neural networks have been used in a continual running fully learning algorithm. The article discusses various studies and developments in the field of artificial intelligence and machine learning. These include visual reasoning with a general conditioning layer, long short-term memory, multimodal language models, embodied learning with a human in the loop, and imitation learning. The article also touches on concerns about AI and its potential risks, as well as the need to create safe and open-ended AI. Lastly, the article highlights the importance of developing zero-shot planners and language models as tools for embodied agents. This document discusses the concept of "thought cloning," which involves training robots to think while they act. The authors reference various studies and theories related to learning and language, as well as robotics and artificial intelligence. The work was supported by the Vector Institute, Schmidt Futures, and NSERC Discovery Grant, among others. The authors express gratitude to colleagues and donors who contributed to the project. They suggest that thought cloning could lead to advancements in artificial general intelligence, AI safety, and interpretability. The article discusses Thought Cloning, a method of training agents to think like humans while acting. This approach involves using datasets of humans thinking out loud while performing tasks, allowing agents to not only learn actions but also high-level thinking. The benefits of this approach include interpretability, safety, and improved AI capabilities such as planning and reasoning. The use of internet-scale datasets is also highlighted as a potential way to enhance agent performance. The article provides empirical evidence of the benefits of Thought Cloning in comparison to other methods such as Behavioral Cloning. The potential applications of this approach include collaboration with humans on complex tasks and AI safety. Thought Cloning Learning to Think while Acting is a study that explores the value of datasets that align action with language. The study examines various works in the literature, including SL3, which generates a hierarchical dataset for agents to learn from, and PALM-E, where a pre-trained Vision-Language Model is adopted as the planner. The study also looks at works that involve pre-trained LLMs that generate plans in language for RL systems. The study proposes augmenting the approach by enabling agents to think in language, facilitating the capability of TC agents in effectively collaborating with humans to accomplish challenging missions. The study finds that the TC agent, when provided with oracle high-level thoughts, is capable of near-perfect performance across almost all environments. Thought Cloning (TC) is a method that enables steerability and interpretability in AI agents by conditioning their actions on their thoughts. The model's interpretability aids in diagnosing problems and simplifying the development of more capable and safer AI. To demonstrate the flexibility of TC agents, a Precrime Intervention feature was developed to prevent unsafe behaviors. This feature can be customized to different settings and does not require changes to the weights of the model. TC agents show promising potential in advancing AI safety. The article presents a study on the effectiveness and interpretability of Thought Cloning (TC) agents in preventing unsafe behaviors. The study found that Precrime Intervention effectively eliminates almost all unsafe behaviors, with touching red items being the most dangerous plan. The Future Action Declaration Score was introduced as a metric to evaluate the interpretability of TC agents. The study also compared the performance of TC and Behavior Cloning (BC) agents in adapting to out-of-distribution environments, and found that TC agents outperformed BC agents. The study concludes that leveraging internet-sized datasets of human thinking can enhance the power of TC agents, making them more capable in high-level thinking. The study compares the performance of Thought Cloning (TC) and Behavioral Cloning (BC) agents on environments that are increasingly out of distribution. The results show that TC generalizes much better than BC, achieving near-optimal performance even on the most challenging environments. The study defines out-of-distribution (OOD) environments as those with a Behavioral Difficulty greater than 425 or a Cognitive Difficulty of 9. The study evaluates agents on these OOD environments and finds that the TC agent substantially outperforms the BC agent with environments being increasingly out of distribution. The study also observes that the Oracle Thoughts + TC Learned Control enhances agents' generalization capabilities. The study defines Cognitive Difficulty and Behavioral Difficulty for the environments and calculates them using a formula adapted from the maxStep parameter calculation in BabyAI environments. The study groups the environments into sets based on their difficulty levels and evaluates agents' zero-shot and fine-tuning performances on them. The results show that TC agents perform better than BC agents across all testing difficulties. The article compares the performance of Thought Cloning (TC) and Behavioral Cloning (BC) agents in solving environments with different levels of difficulty. Difficulty is based on the length of the action sequence required to solve the environment and is divided into two dimensions: Behavioral Difficulty and Cognitive Difficulty. TC outperforms BC in solving environments that are increasingly out of distribution and also demonstrates successful planning and replanning abilities. TC's superior performance is not solely due to a larger number of parameters than BC, as evidenced by an ablation variant TC w/o Imitating Thought that shares the same architecture with TC but without the Thought Cloning loss in training. The results show that TC learns faster than BC and ultimately outperforms it. The article also highlights the interpretability benefits of TC, as it is easy to follow along and understand why the agent executes certain actions. Additionally, the study supports the hypothesis that learning from human thought boosts an agent's ability to think. This text describes the results of a study comparing the performance of Thought Cloning (TC) and Behavioral Cloning (BC) in terms of learning speed. The TC approach uses imitation learning and thought supervision to improve performance, while the BC approach relies solely on imitation learning. During testing, TC and BC agents were tested in 512 environments, and success was defined as completing all specified tasks in the mission. Results showed that TC learned faster than BC, with a higher success rate. The training setup was based on BabyAI, with slight differences in architecture between TC and BC. To generate a thought dataset, an Oracle Solver was used to translate internal states into natural language thoughts using predefined rules. The main results were produced using ten A40 GPUs over one week. This paper focuses on using BabyAI, a simulated partially observable 2D gridworld domain, to generate step-by-step solutions for challenging missions. The missions consist of multiple tasks that require complicated navigation and actions, and are described in natural language. The key challenges in BabyAI include partial observability, hard-to-explore mazes, and complex missions. The agent's action space includes left, right, forward, pickup, drop, toggle door (unlock, open, close), and occluded grid cells are assigned an item ID of 0. Colored items and the initial position of the agent are randomly distributed across a 27 x 27 grid world containing nine 3 x 3 rooms. The agent can pick up, drop, and move objects or open and close doors, while locked doors can only be unlocked with color-matched keys. The agent's thoughts and actions show replanning when encountering obstacles. The paper discusses a training framework called Thought Cloning, which aims to teach agents how to think while acting by utilizing a synchronized dataset of human thinking and action. The framework comprises an Upper-level Component responsible for thought generation and a Lower-level Component tasked with executing actions based on the thoughts generated by the Upper-level Component. In the Thought Cloning training framework, agents learn to produce natural language thoughts at each timestep and subsequently condition their actions based on these generated thoughts. The model also employs FiLM for modality fusion to address the partial observability challenge. The architecture adopted in this paper can be effectively combined with pre-trained Vision-Language Models (VLM) either zero-shot or fine-tuned. The coefficients for Thought Cloning loss, th, o, a, and m, denote thought, observation, action, and mission, respectively. The model can be trained from scratch or adapted from existing language-conditioned controllers in the target domain. The framework can be implemented for more complex or large-scale scenarios, as previously described. The proposed method, Thought Cloning, is an imitation learning framework where agents learn to act and think from demonstrations. The TC agent has two components: the Upper-Level Component generates thoughts and the Lower-Level Component generates actions conditioned on these thoughts. The TC agent receives an observation and a history of thoughts as inputs. The results show that Thought Cloning outperforms Behavioral Cloning in out-of-distribution tasks in both zero-shot and fine-tuning settings. The framework offers significant potential for AI safety and interpretability, where unsafe behavior can be near perfectly stopped before execution. The thought data, such as YouTube videos and transcripts, contains millions of hours of people talking out loud while performing tasks, revealing the thinking behind their actions, planning, decisions, and replanning. This thought data is greatly valuable and widely available. The approach is distinct from existing works that leverage pre-trained Large Language Models (LLMs) for planning because such LLMs are not trained on data where humans think out loud while acting. The ability for AI agents to think in language has significant advantages, including improved AI training and the ability to spot and debug issues. Agents that think in human language are also easier to train for challenging tasks, and watching agents think enhances their steerability. Additionally, agents that think in language may learn faster, perform better, and generalize better than non-lingual agents. Language helps humans generalize and extrapolate, and agents that can understand language allow us to define new tasks at test time without having to anticipate every wish we might eventually have for the task through trial and error. The benefits of language are not confined to improving our ability to communicate with others but also help us think better. Thought Cloning is an Immitation Learning framework that trains AI agents to think like humans do in addition to behaving like them. By observing the agents' thoughts, it becomes easier to diagnose and fix problems, prevent unsafe behavior, and improve their ability to handle novel situations. Thought Cloning learns much faster than Behavioral Cloning and performs exceptionally well on internet-scale datasets of humans thinking out loud while acting. Language is a key aspect of human thinking that provides us with exceptional abilities to generalize, explore, plan, replan, and adapt to new situations. Thought Cloning aims to bridge the gap between human and AI thinking to create safer and more powerful agents.