What are the key components of the Dyna architecture in reinforcement learning?
Another important aspect of Dyna is the use of a prioritized sweeping technique, where the planner focuses on exploring and updating the most influential states that have the potential to impact the agent's learning significantly.
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In summary, Dyna combines model-based planning and model-free reinforcement learning to improve the agent's efficiency in exploring the environment and optimizing its policy.
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The Dyna architecture in reinforcement learning consists of three main components: a model, a planner, and an agent. The model is responsible for representing the environment and its dynamics, allowing the agent to simulate different scenarios and learn from them. The planner uses the model to generate plans or sequences of actions based on the current state and desired goals. The agent then executes these plans, interacts with the environment, and updates its policy based on the observed rewards and outcomes.
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