Reinforcement Learning using Unity 3D.

Reinforcement learning differs from the supervised learning in a way that in supervised learning the training data has the answer key with it so the model is trained with the correct answer itself whereas in reinforcement learning, there is no answer but the reinforcement agent decides what to do to perform the given task. In the absence of training dataset, it is bound to learn from its experience.

Types of Reinforcement: There are two types of Reinforcement:

Positive –
Positive Reinforcement is defined as when an event, occurs due to a particular behavior, increases the strength and the frequency of the behavior. In other words it has a positive effect on the behavior.

Negative –
Negative Reinforcement is defined as strengthening of a behavior because a negative condition is stopped or avoided.

Reinforcement Learning

Various Practical applications of Reinforcement Learning –

RL can be used in robotics for industrial automation.
RL can be used in machine learning and data processing
RL can be used to create training systems that provide custom instruction and materials according to the requirement of students.

Unity Machine Learning Agents beta

Unity Machine Learning Agents, the first of Unity’s machine learning product offerings, trains intelligent agents with reinforcement learning and evolutionary methods via a simple Python API.

Demo Application –

Spider bot self-learning to walk

Reinforcement Learning 3

Reinforcement Learning 2

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