A brief about Traditional transformer architectures

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Action Transformer is a deep learning architecture that combines the strengths of both transformers and reinforcement learning to solve complex decision-making problems. The Action Transformer architecture was introduced in a research paper published in 2020 by a team of researchers from Google Research and DeepMind.



In traditional transformer architectures, the input sequence is transformed into a sequence of hidden states using a self-attention mechanism. These hidden states are then used to generate output sequences. In contrast, the Action Transformer architecture incorporates a reinforcement learning component, where the model learns to make decisions by taking actions in an environment and receiving rewards.

The Action Transformer architecture consists of three main components: a transformer network, a policy network, and a value network. The transformer network is responsible for processing the input sequence and generating a sequence of hidden states. The policy network takes these hidden states as input and outputs a probability distribution over the possible actions that the model can take. The value network estimates the expected reward that the model will receive if it takes a particular action.

During training, the model receives a state from the environment, uses the transformer network to generate hidden states, and then uses the policy network to select an action based on the current state. The model then receives a reward from the environment and updates the parameters of the policy and value networks using backpropagation.

One of the key advantages of the Action Transformer architecture is that it can learn to make decisions in complex environments with high-dimensional state spaces and large action spaces. The architecture has been applied to a range of tasks, including video game playing, robotics, and natural language processing.

 In summary, the Action Transformer architecture is a powerful deep learning architecture that combines the strengths of transformers and reinforcement learning to enable decision-making in complex environments. With its ability to handle high-dimensional state spaces and large action spaces, the Action Transformer architecture has the potential to advance the state-of-the-art in a range of domains.


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