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Yair Shinar
Deliverer of Efficient Results
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Simple Bicycle Model – How a vehicle moves given a steering angle

2019-12-15
On: December 15, 2019
In: Autonomous Vehicles, Cars, Drones, Math, Physics, Science

Pseudo code with arbitrary values of a Simple Bicycle Model given steering change. The x-axis points to the forward direction or the longitudinal direction The y-axis, which represents the lateral direction is positive when it points to the right of the driver The z-axis points to the ground satisfying theContinue Reading

Hidden Markov Model (HMM)

2019-09-08
On: September 8, 2019
In: AI, Algorithms, Autonomous Vehicles, Cars, Computer Vision, Drones, NLP, Reinforcement Learning

Hidden Markov Model (HMM) is a statistical Markov model in which the system being modeled is assumed to be a Markov process with unobservable (i.e. hidden) states. In simpler Markov models (like a Markov chain), the state is directly visible to the observer, and therefore the state transition probabilities areContinue Reading

Markov Chain

2019-09-08
On: September 8, 2019
In: AI, Algorithms, Autonomous Vehicles, Cars, Computer Vision, Drones, NLP, Reinforcement Learning

A Markov chain is a stochastic model describing a sequence of possible events in which the probability of each event depends only on the state attained in the previous event. (“memorylessness”)Continue Reading

Q-learning – Model-free reinforcement learning algorithm

2019-07-06
On: July 6, 2019
In: AI, Algorithms, Deep Machine Learning, Reinforcement Learning

Learns a policy which tells an agent what action to take under what circumstances. Q-learning learns a policy that is optimal in the sense that it maximizes the expected value of the total reward over any and all successive steps, starting from the current state.Continue Reading

Markov decision process (MDP) – Reinforcement Learning decision model

2019-07-06
On: July 6, 2019
In: AI, Deep Machine Learning, Reinforcement Learning

* Is a discrete time stochastic control process for decision making in situations where outcomes are partly random and partly under the control of a decision maker. * At each discrete time step, the process is in some state s, and the decision maker may choose any action a thatContinue Reading

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Yair Shinar

Clarifying the need → Finding the solution → Executing → Optimizing

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