- Machine Learning Basics
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- Machine Learning - Supervised vs. Unsupervised
- Machine Learning Data Visualization
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- Statistics for Machine Learning
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- Regression Analysis In ML
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- Classification Algorithms In ML
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- Machine Learning - Naïve Bayes Algorithm
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- Clustering Algorithms In ML
- Machine Learning - Clustering Algorithms
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- Machine Learning - Agglomerative Clustering
- Dimensionality Reduction In ML
- Machine Learning - Dimensionality Reduction
- Machine Learning - Feature Selection
- Machine Learning - Feature Extraction
- Machine Learning - Backward Elimination
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- Machine Learning - Discussion
Machine Learning - Reinforcement
These methods are a bit different from previously studied methods and very rarely used also. In this kind of learning algorithms, there would be an agent that we want to train over a period of time so that it can interact with a specific environment. The agent will follow a set of strategies for interacting with the environment and then after observing the environment it will take actions regards the current state of the environment.
Here are the major steps involved in reinforcement learning methods −
Step 1 − First, we need to prepare an agent with some initial set of strategies.
Step 2 − Then observe the environment and its current state.
Step 3 − Next, select the optimal policy regards the current state of the environment and perform important action.
Step 4 − Now, the agent can get corresponding reward or penalty as per accordance with the action taken by it in previous step.
Step 5 − Now, we can update the strategies if it is required so.
Step 6 − At last, repeat steps 2-5 until the agent got to learn & adopt the optimal policies.
The following diagram shows what type of task is appropriate for various ML problems −
