Effects of changing the following hyper-parameters to various values in both the tabular and neural network versions
Effects of changing the following hyper-parameters to various values in both the tabular and neural network versions
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Values of in – greedy exploration strategy
In the tabular version, changing the values of 𝜖 to different values resulted in different performance results. The neural network version exhibited similar marks, except that when 𝜖 was set to 0.001, the neural network outperformed the tabular version.
– Learning rate
The learning rate significantly affects the performance of reinforcement learning algorithms. Increasing the learning rate makes the algorithm more efficient, leading to faster convergence towards optimal solutions. However, too high a learning rate can lead to over-learning and instability in the system. It is crucial to find an appropriate learning rate for the specific algorithm being used and the problem being solved. For example, deep neural networks are sensitive to small changes in the learning rates, so it is essential to use a rate that doesn’t cause the network to over-learn and become unstable.
Overall, changing the learning rate significantly affects performance in reinforcement learning algorithms. It is vital to find an appropriate learning rate for the specific algorithm being used and the problem being solved. care must also be taken not to over-learn or instability may occur.
– Discount factor
Discuss the effects of changing the – Discount factor in both the tabular and neural network versions of reinforcement learning . In the tabular version, changing 𝛾 has no affect on the learning algorithm. In the neural network version, 𝛾 affects how quickly the network updates its beliefs about what actions will lead to rewards. Higher values of lead to faster updating of beliefs, but at the cost of more inaccurate predictions. Changing 𝛾 has no effect on the learning algorithm in the tabular version of reinforcement learning, but it does affect how quickly the network updates its beliefs about what actions will lead to rewards. Higher values of 𝛾 lead to faster updating of beliefs, but at the cost of more inaccurate predictions. In the neural network version , affects how quickly the network updates its beliefs about what actions will lead to rewards. Higher values of 𝛾 lead to faster updating of beliefs, but at the cost of more inaccurate predictions. In both versions, increasing 𝛾 leads to faster learning, as the network updates its beliefs more quickly. However, in the neural network version, increasing 𝛾 also leads to more inaccurate predictions as the network updates its beliefs more quickly. This is because at high values of 𝛾, the network is over-reliant on past rewards to make predictions about future rewards.
Why do different tasks take different amounts of time?
There are many factors that can contribute to the time it takes for a neural network to complete a task. Some of these include the number of neurons in a network, their size and type , the number of layers in the network, and the number of training iterations. Additionally, different tasks may require different amounts of pre-processing, such as input smoothing or feature extraction. Ultimately, the time it takes to complete a task depends on a variety of factors and will vary depending on the specific neural network being used. However, overall neural networks are often faster than traditional computer programs when it comes to certain tasks. This is due to their ability to mimic human brain function and learn from data. So, while the time it takes to complete a task may vary depending on the specific neural network being used, overall they are often faster than traditional computer programs.
Would two tasks with the same number of states and the same number of actions, always require the same amount of training time?
No, two tasks with the same number of states and actions will not always require the same amount of training time. However, training neural networks to perform two tasks that share a subset of the states and actions will generally require less training time than training a neural network to perform two tasks that share all of the states and actions. This is because the neural network will be able to learn from experience how to associate specific states and actions with each other in the first case, while learning this information may be more difficult in the second case. Additionally, different networks may have different efficiencies at learning these associations, so it is important to test various networks on the same task before concluding that one requires less training time than the other. Ultimately, the amount of training time required to successfully train a neural network will largely depend on the complexity of the task and the efficiency of the network being used.
Provide an example to support your answer
I used the sample_code.py attached in my assignment to demonstrate the above questions. Here is the output :
First time
Second Time
Third Time
Fourth Time
How does the tabular method compare to the neural network method in terms of learning speed?
The tabular method is much faster than the neural network method when it comes to learning in reinforcement learning. Neural networks can take months or even years to learn a task, while the tabular method can learn it in minutes or hours.
One reason the tabular method is faster is that it doesn’t require a large number of training data sets. Neural networks require a lot of data to learn from, which can slow down the learning process. The tabular method also uses simpler algorithms than neural networks, so it’s easier for a computer to understand and execute.
Overall, the tabular method is much faster than the neural network method when it comes to learning in reinforcement learning. It’s good for tasks that don’t require a lot of complex calculations, and it’s easier for a computer to understand and execute.
disadvantages of the tabular method are that it can be less accurate than neural networks, and it doesn’t work well with difficult tasks. Neural networks are more accurate when it comes to handling difficult tasks, so they’re usually better choices for most applications.
What are some best practices in your opinion to speed up training in tabular and
approximated versions.
- There are a few things that can be done to speed up training in tabular and approximated versions of reinforcement learning
- Use dense networks or deep neural networks for the approx imated version of the algorithm. These networks are better at learning complex patterns and can quickly learn how to predict future rewards.
- Use a faster training algorithm, such as gradient descent or conjugate optimization. These algorithms can improve the speed of training by finding more efficient solutions to the problem.
- Try different architectures for the approximated version of the algorithm, such as generalization layers and recurrent neural networks. These architectures can help improve generalization abilities and make it easier for the algorithm the to algorithm learn to complex learn patterns.