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A glimpse of the general agent–GATO


Published on: June 21, 2023





Using a similar strategy to create a single generalist agent outside the scope of text outputs, motivated by advancements in large-scale language modeling. In fact, this is something that we humans do as well. We have the capability to use existing knowledge of a different domain to achieve satisfactory results in new and unfamiliar tasks.


Inspired by the human capabilities engineers at DeepMind made a model called Gato. This agent functions as a generalist policy that can handle multiple tasks and multiple modalities.


Gato has a common network, with the general weights, that can play Atari, caption pictures, talk, stack blocks using a real robot arm, and do a lot more. It decides whether to output text, joint torques, button presses, or other tokens depending on the context. The model, the data, and the current Gato capabilities are described in this study.



Reed, S., Zolna, K., Parisotto, E., Colmenarejo, S. G., Novikov, A., Gimenez, M., Sulsky, Y., Kay, J., Springenberg, J. T., Eccles, T., Bruce, J., Razavi, A., Edwards, A., Heess, N., Chen, Y., Hadsell, R., Vinyals, O., Bordbar, M., & de Freitas, N. (2022). A Generalist Agent. ArXiv. /abs/2205.06175

A generalist agent – Gato can sense and act with different embodiments across a wide range of environments using a single neural network with the same set of weights. Gato was trained on 604 distinct tasks with varying modalities, observations, and action specifications. This agent is designed to be flexible and adaptable and is able to learn and operate in a variety of environments and situations.


When compared to a specialist or a domain-specific agent, which is designed to perform a specific task, a generalist agent has a wider range of abilities and can handle a broader range of tasks in a larger spectrum. This can make it more versatile and better suited to complex or dynamic environments where a specialist agent may be limited.


Now to assume how such a model can be built we must first understand primary topics such as reinforcement learning, where the agent learns from trial and error in a variety of environments, and meta-learning, where the agent learns how to learn and adapts its learning strategy to different tasks and environments. But there can be other methods as well for instance,

  1. Transfer learning-based agents: Transfer learning is a technique where knowledge learned in one domain is transferred to another domain. Generalist agents based on transfer learning are designed to learn from multiple related tasks and can apply the knowledge learned from one task to another.

  2. Hybrid agents: Hybrid generalist agents combine multiple techniques to create a more flexible and adaptable agent. For example, a hybrid agent might use reinforcement learning to learn from a variety of tasks and transfer learning to apply the knowledge learned to new tasks.

But we must also keep in mind that there is no one-size-fits-all solution to building a generalist agent in AI, and different techniques may be more or less suitable depending on the specific task or domain. Researchers continue to explore new approaches to building generalist agents that can perform a wide range of tasks in a variety of environments. As such let us now explore some of the topics that we discussed briefly.

Reinforcement learning-based agents

Reinforcement learning (RL) is a type of machine learning where an agent learns to make decisions by interacting with an environment and receiving feedback in the form of rewards or punishments also called regularization. RL-based agents have been successfully applied in a variety of applications, including robotics, game playing, and finance.


The basic idea behind RL is that an agent is placed in an environment and is given a set of actions it can take. When the agent takes an action, it receives a reward or penalty from the environment based on the outcome of that action. The goal of the agent is to learn a policy that maps states to actions in a way that maximizes its cumulative reward over time.



Arulkumaran, K., Deisenroth, M. P., Brundage, M., & Bharath, A. A. (2017). A Brief Survey of Deep Reinforcement Learning. ArXiv. https://doi.org/10.1109/MSP.2017.2743240


RL algorithms can be classified into two main categories: model-based and model-free. Model-based algorithms learn a model of the environment, which allows them to predict the outcome of actions and plan accordingly.


Model-free algorithms, on the other hand, directly learn a policy without building a model of the environment.


One of the most popular RL algorithms is Q-learning, which is a model-free algorithm that learns a state-action value function called the Q-function.


The Q-function estimates the expected cumulative reward for taking a particular action in a particular state and is updated iteratively as the agent interacts with the environment.


Another popular RL algorithm is policy gradient, which directly learns a policy by optimizing a performance metric such as the expected cumulative reward.


RL-based agents can be trained using various techniques such as deep learning and imitation learning. Deep reinforcement learning (DRL) combines RL with deep neural networks, allowing agents to learn more complex and high-dimensional policies.


Imitation learning, on the other hand, involves training an agent to imitate the behavior of an expert by learning from their demonstrations.


With the advancement in computing hardware and evolution in software engineering, it is certain that reinforcement learning is the way to future algorithmic design and approach with other components shaping this frontier.


Transfer learning-based agents

Transfer learning is a machine learning technique that allows an agent to transfer knowledge learned in one task or domain to another task or domain. Transfer learning-based agents have been successfully applied in many applications where data is limited or the target domain is different from the source domain.


In transfer learning, an agent is first trained on a source task or domain that has abundant data and can be quickly learned. The knowledge learned by the agent in the source task or domain is then transferred to the target task or domain, where the agent continues learning with a smaller amount of data. This approach can significantly reduce the training time and improve the performance of the agent in the target task or domain.


There are several types of transfer learning techniques that can be used to train transfer learning-based agents, including:

  • Inductive transfer learning: In this technique, the knowledge learned in the source task or domain is directly transferred to the target task or domain without any modification.

  • Multi-task learning: In this technique, the agent is trained to perform multiple related tasks simultaneously, and the knowledge learned from these tasks is transferred to the target task or domain.

  • Domain adaptation: In this technique, the agent adapts the knowledge learned in the source domain to the target domain by modifying the model or the data to match the target domain.

  • Pre-training: In this technique, the agent is pre-trained on a large dataset in the source domain, and the knowledge learned from the pre-training is then fine-tuned on the target domain.


Transfer learning-based agents can be trained using various machine learning approaches, including deep neural networks, reinforcement learning, and supervised learning. Deep transfer learning, which combines deep learning with transfer learning, has been particularly successful in many applications, such as image recognition and natural language processing.


Meta-learning-based agents

Meta-learning is a machine learning technique that enables an agent to learn how to learn, by learning to quickly adapt to new tasks or environments. Meta-learning-based agents have shown great potential in a wide range of applications, including robotics, natural language modeling, and computer vision.


In meta-learning, an agent is trained on a set of tasks or environments and learns to adapt its policy to new tasks or environments more quickly and efficiently than if it had to start from scratch each time. The agent is trained to learn a meta-policy, which is a policy that takes the task or environment as input and outputs a policy that is optimized for that task or environment.


There are several approaches to meta-learning, including:

  • Model-based meta-learning: In this approach, the agent learns a model of the environment or task, which it can use to quickly generate a policy that is optimized for that task or environment.

  • Metric-based meta-learning: In this approach, the agent learns a metric that can be used to compare the similarity between tasks or environments, and uses this metric to quickly generate a policy that is optimized for the new task or environment.

  • Optimization-based meta-learning: In this approach, the agent learns to optimize its policy using gradient-based methods, such as stochastic gradient descent, to quickly generate a policy that is optimized for the new task or environment.


Meta-learning-based agents can also be trained using various machine-learning approaches similar to reinforcement learning approaches. Deep meta-learning, which combines deep learning with meta-learning, has been particularly successful in many applications, such as image recognition and robotics.


Hybrid agents

Hybrid agents are artificial agents that combine multiple types of machine learning techniques, such as reinforcement learning, deep learning, transfer learning, and meta-learning, to solve complex problems. Hybrid agents are becoming increasingly popular in many applications, such as robotics, autonomous vehicles, and healthcare.


The advantage of using a hybrid approach is that it can leverage the strengths of different machine-learning techniques and compensate for their weaknesses. For instance, deep learning can be used for perception tasks, such as image or speech recognition, while reinforcement learning can be used for decision-making tasks, such as navigation or game playing.


Hybrid agents can be designed in various ways, depending on the problem at hand. Here are a few examples:

  • Reinforcement learning with deep neural networks leverages the power of deep neural networks trained to extract relevant features from the environment, which are then fed into the reinforcement learning algorithm to make decisions.

  • Transfer learning with meta-learning combines the power of transfer learning for leveraging knowledge from related tasks or domains with meta-learning for quickly adapting to new tasks or domains. The agent is first pre-trained on a set of related tasks or domains and then meta-trained to quickly adapt to new tasks or domains.

  • Reinforcement learning with unsupervised learning: This approach combines the power of reinforcement learning for decision-making with unsupervised learning for learning representations of the environment. The agent learns to predict the future state of the environment using unsupervised learning, which can help it make better decisions using reinforcement learning.


The following example shows that hybrid agents have shown great potential in solving complex problems by combining the strengths of multiple machine-learning techniques. However, designing and training hybrid agents can be challenging, as it requires a deep understanding of the problem at hand and the strengths and weaknesses of different machine learning techniques.

Training

The training of a generalist agent depends on the specific context and domain in which it will be operating. However, in general, the agent is trained to be able to perform a wide range of tasks across different domains, rather than being specialized in a single task or domain.


As we have discussed the two common approaches used to create such a model are reinforcement learning and meta-learning.


In both cases, the training process typically involves exposing the agent to a large and diverse set of tasks and environments, in order to encourage it to develop a flexible and generalizable policy that can perform well across different domains. The exact details of the training process will depend on the specific algorithms and techniques used, as well as the particular goals and constraints of the application.



Reed, S., Zolna, K., Parisotto, E., Colmenarejo, S. G., Novikov, A., Gimenez, M., Sulsky, Y., Kay, J., Springenberg, J. T., Eccles, T., Bruce, J., Razavi, A., Edwards, A., Heess, N., Chen, Y., Hadsell, R., Vinyals, O., Bordbar, M., & de Freitas, N. (2022). A Generalist Agent. ArXiv. /abs/2205.06175

The training phase of GATO specifically the data from different tasks and modalities is serialized into a flat sequence of tokens, batched, and processed by a transformer neural network akin to a large language model. Masking is used such that the loss function is applied only to target outputs, i.e. text and various actions.



Reed, S., Zolna, K., Parisotto, E., Colmenarejo, S. G., Novikov, A., Gimenez, M., Sulsky, Y., Kay, J., Springenberg, J. T., Eccles, T., Bruce, J., Razavi, A., Edwards, A., Heess, N., Chen, Y., Hadsell, R., Vinyals, O., Bordbar, M., & de Freitas, N. (2022). A Generalist Agent. ArXiv. /abs/2205.06175


GATO is being used as a control policy. GATO creates the subsequent action in a conventional autoregressive fashion by consuming a series of interleaved tokenized observations, separator tokens, and previously sampled actions.


A new set of observations is made once the new action is applied to the environment, in this case, a video game console and the cycle is repeated.

Gato and the Future of AI

A generalist agent (GATO) is an artificial agent that is capable of performing multiple tasks or solving problems in multiple domains, without being explicitly trained for each task or domain. Generalist agents are considered to be a key component of the future of AI, as they have the potential to make AI more versatile and adaptable to new and unforeseen situations.

The development of generalist agents is still in its early stages, and much research is needed to achieve this goal. However, there are several promising directions for future research in this area:

  • Multi-task learning: Multi-task learning is a technique where an agent is trained on multiple tasks simultaneously, which can improve its ability to generalize to new tasks. Generalist agents that are capable of multi-task learning can adapt to new situations more quickly and efficiently.

  • Transfer learning: Transfer learning is a technique where an agent transfers knowledge learned in one task or domain to another task or domain. Generalist agents that are capable of transfer learning can leverage knowledge from previous tasks or domains to improve their performance on new tasks or domains.

  • Meta-learning: Meta-learning is a technique where an agent learns how to learn, by learning to quickly adapt to new tasks or environments. Generalist agents that are capable of meta-learning can learn to adapt to new situations more quickly and efficiently.

  • Unsupervised learning: Unsupervised learning is a technique where an agent learns to extract useful information from unlabeled data, which can be used to improve its ability to generalize to new situations. Generalist agents that are capable of unsupervised learning can learn from large amounts of data without the need for explicit labels.


The development of generalist agents has the potential to transform many industries, such as healthcare, finance, and transportation, by enabling AI to solve a wide range of problems and adapt to new situations. However, there are also concerns about the potential risks of generalist agents, such as the possibility of unintended consequences or misuse. Therefore, it is important to continue research in this area with a focus on developing safe and ethical AI.

Conclusion

An artificial intelligence system that can carry out various jobs or provide solutions to issues in several different fields is known as a generalist agent. A potential field of AI research is the creation of generalist agents, which can increase AI's versatility and capacity to respond to novel and unanticipated situations. Future research in this field should focus on unsupervised learning, meta-learning, transfer learning, and multi-task learning. Several sectors could be transformed by the creation of generalist agents, yet there are also worries about possible risks like unexpected consequences or misuse. Consequently, it is crucial to carry on with research into creating ethical and safe AI.


A transformer neural network, resembling a big language model, is used by GATO to serialize data from various activities and modalities into a flat series of tokens.

References

  1. Reed, S., Zolna, K., Parisotto, E., Colmenarejo, S. G., Novikov, A., Gimenez, M., Sulsky, Y., Kay, J., Springenberg, J. T., Eccles, T., Bruce, J., Razavi, A., Edwards, A., Heess, N., Chen, Y., Hadsell, R., Vinyals, O., Bordbar, M., & de Freitas, N. (2022). A Generalist Agent. ArXiv. /abs/2205.06175

  2. Castagna, A., & Dusparic, I. (2021). Multi-Agent Transfer Learning in Reinforcement Learning-Based Ride-Sharing Systems. ArXiv. /abs/2112.00424

  3. Gupta, A., Lanctot, M., & Lazaridou, A. (2021). Dynamic population-based meta-learning for multi-agent communication with natural language. ArXiv. /abs/2110.14241

  4. Enders, T., Harrison, J., Pavone, M., & Schiffer, M. (2022). Hybrid Multi-agent Deep Reinforcement Learning for Autonomous Mobility on Demand Systems. ArXiv. /abs/2212.07313

  5. Achiam, Joshua, Spinning Up in Deep Reinforcement Learning, 2018


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