Contents
What is an observation in reinforcement learning?
Observational learning is a type of learning that occurs as a function of observing, retaining and possibly replicating or imitating the behaviour of another agent. Especially, we argue that observational learning can emerge from pure Reinforcement Learning (RL), potentially coupled with memory.
What is observation in machine learning?
In machine learning, the observations are often known as instances, the explanatory variables are termed features (grouped into a feature vector), and the possible categories to be predicted are classes.
What is the null accuracy?
Any irrelevant information or randomness in a dataset which obscures the underlying pattern. Null Accuracy. Baseline accuracy that can be achieved by always predicting the most frequent class (“B has the highest frequency, so lets guess B every time”).
What is variable in machine learning?
Dependent variables are nothing but the variable which holds the phenomena which we are studying. Independent variables are the ones which through we are trying to explain the value or effect of the output variable (dependent variable) by creating a relationship between an independent and dependent variable.
How is an observation used in reinforcement learning?
An observation is some data that you can collect. E.g. you may have sensors on your robot that feed back the positions of its joints. Because the state should possess the Markov Property, a single raw observation might not be enough data to make a suitable state.
How to define States in a reinforcement learning system?
This is due to the ” curse of dimensionality “. For those problems, you will typically represent your state as a vector of different features – e.g. for a robot, various positions, angles, velocities of mechanical parts. As with supervised learning, you may want to treat these for use with a specific learning process.
What are the advantages and disadvantages of reinforcement learning?
Advantages of reinforcement learning are: Maximizes Performance. Sustain Change for a long period of time. Disadvantages of reinforcement learning: Too much Reinforcement can lead to overload of states which can diminish the results.
When is the state space the same as the observation space?
Partial Observation: when the agent is able to observe only partial information regarding the state of the environment. Set of all States or the State Space can be considered same as the Observation space if the environment is completely observable (and without noise ).