Dyna is an AI architecture that integrates learning, planning, and reactive execution. It uses machine learning methods for learning from examples, but is not tied to any particular method. The architecture is designed for cases where the agent does not have complete knowledge of the effects of its actions on the world, and those effects may be non-deterministic. Dyna assumes the agent's task can be formulated as a reward maximization problem. At each time step, the agent observes a situation, takes an action, and then observes a reward and new situation. The agent's objective is to choose actions to maximize long-term rewards. The core idea of Dyna is that planning is "trying things in your head" using an internal model of the world. This suggests a more primitive process of trying things through direct interaction with the world, which is known as reinforcement learning. Dyna extends reinforcement learning to include a learned world model. The Dyna architecture consists of three main components: the structure and learning of the action model, an algorithm for selecting hypothetical states and actions, and a reinforcement learning method. The action model is a black box that predicts the results of actions. The algorithm uses reinforcement learning to learn the optimal reactive policy. The fifth step of the algorithm is essentially reinforcement learning from hypothetical experiences, which is a planning process. Dyna is fully reactive, meaning no planning intervenes between perception and action. The architecture is based on dynamic programming and related methods. It has been shown that IDP planning can greatly speed the finding of the optimal policy. However, IDP planning may require large amounts of memory. Potential problems with Dyna include reliance on supervised learning, hierarchical planning, ambiguous and hidden states, ensuring variety in behavior, taskability, and incorporation of prior knowledge. Despite these challenges, Dyna offers a flexible and efficient method for learning and planning. The architecture has been tested in various applications, including robotic tasks and learning from examples.Dyna is an AI architecture that integrates learning, planning, and reactive execution. It uses machine learning methods for learning from examples, but is not tied to any particular method. The architecture is designed for cases where the agent does not have complete knowledge of the effects of its actions on the world, and those effects may be non-deterministic. Dyna assumes the agent's task can be formulated as a reward maximization problem. At each time step, the agent observes a situation, takes an action, and then observes a reward and new situation. The agent's objective is to choose actions to maximize long-term rewards. The core idea of Dyna is that planning is "trying things in your head" using an internal model of the world. This suggests a more primitive process of trying things through direct interaction with the world, which is known as reinforcement learning. Dyna extends reinforcement learning to include a learned world model. The Dyna architecture consists of three main components: the structure and learning of the action model, an algorithm for selecting hypothetical states and actions, and a reinforcement learning method. The action model is a black box that predicts the results of actions. The algorithm uses reinforcement learning to learn the optimal reactive policy. The fifth step of the algorithm is essentially reinforcement learning from hypothetical experiences, which is a planning process. Dyna is fully reactive, meaning no planning intervenes between perception and action. The architecture is based on dynamic programming and related methods. It has been shown that IDP planning can greatly speed the finding of the optimal policy. However, IDP planning may require large amounts of memory. Potential problems with Dyna include reliance on supervised learning, hierarchical planning, ambiguous and hidden states, ensuring variety in behavior, taskability, and incorporation of prior knowledge. Despite these challenges, Dyna offers a flexible and efficient method for learning and planning. The architecture has been tested in various applications, including robotic tasks and learning from examples.