How is the action chosen during each game iteration when using the neural network to predict the action?
During each game iteration when using a neural network to predict the action, the action is chosen based on the output of the neural network. The neural network takes in the current state of the game as input and produces a probability distribution over the possible actions. The chosen action is then selected based on this probability distribution.
To understand how the action is chosen, let's consider the process in more detail. The neural network is trained using a technique called reinforcement learning, specifically a variant known as Q-learning. In this approach, the neural network learns to estimate the expected future rewards for each possible action in a given state.
During training, the neural network is exposed to a large number of game states and corresponding actions. The network learns to adjust its internal parameters in order to maximize the expected future rewards. This is done by minimizing a loss function that quantifies the discrepancy between the predicted rewards and the actual rewards obtained during gameplay.
Once the neural network is trained, it can be used to make predictions during gameplay. Given the current state of the game, the neural network computes a probability distribution over the possible actions. This distribution is typically obtained by applying a softmax function to the output of the neural network.
The softmax function ensures that the probabilities sum up to one and that higher predicted rewards correspond to higher probabilities. This allows the neural network to express its confidence in each possible action based on the expected future rewards.
To choose the action, a random number is generated between 0 and 1. The random number is then compared to the cumulative probabilities of the actions. The action corresponding to the first cumulative probability that exceeds the random number is selected.
For example, suppose the neural network predicts the following probabilities for three possible actions: action A with probability 0.2, action B with probability 0.5, and action C with probability 0.3. If the random number generated is 0.4, the chosen action would be B since the cumulative probability of action A is 0.2 and the cumulative probability of action B is 0.7.
By using this approach, the neural network is able to explore different actions during gameplay and learn from the rewards obtained. Over time, the network improves its predictions and becomes more proficient at selecting actions that lead to higher rewards.
During each game iteration, the action is chosen based on the output of the neural network. The network produces a probability distribution over the possible actions, and the action is selected by comparing a random number to the cumulative probabilities. This approach allows the neural network to learn and improve its predictions over time.
Other recent questions and answers regarding EITC/AI/DLTF Deep Learning with TensorFlow:
- How does the `action_space.sample()` function in OpenAI Gym assist in the initial testing of a game environment, and what information is returned by the environment after an action is executed?
- What are the key components of a neural network model used in training an agent for the CartPole task, and how do they contribute to the model's performance?
- Why is it beneficial to use simulation environments for generating training data in reinforcement learning, particularly in fields like mathematics and physics?
- How does the CartPole environment in OpenAI Gym define success, and what are the conditions that lead to the end of a game?
- What is the role of OpenAI's Gym in training a neural network to play a game, and how does it facilitate the development of reinforcement learning algorithms?
- Does a Convolutional Neural Network generally compress the image more and more into feature maps?
- Are deep learning models based on recursive combinations?
- TensorFlow cannot be summarized as a deep learning library.
- Convolutional neural networks constitute the current standard approach to deep learning for image recognition.
- Why does the batch size control the number of examples in the batch in deep learning?
View more questions and answers in EITC/AI/DLTF Deep Learning with TensorFlow