RL algorithms learn via trial and error. The agent searches the state space early on and takes random actions to learn what leads to a good reward. Pretty straightforward.

RL algorithms learn via trial and error. The agent searches the state space early on and takes random actions to learn what leads to a good reward. Pretty straightforward.

Unfortunately, this isn’t terribly efficient, especially if we already know something about what makes a good vs. bad action in some states. Thankfully, we can use **action masking** — a simple technique that sets the probability of bad actions to 0 — to speed learning and improve our policies.

We enforce constraints via action masking for a knapsack packing environment and show how to do this using RLlib.

Let’s use the classic **knapsack problem** to develop a concrete example.

The knapsack problem (KP) asks you to pack a knapsack to maximize the value in the bag without overloading it. If you have a collection of items like we have shown below, the optimal packing is going to contain three of the yellow boxes and three of the gray boxes for a total of $36 and 15kg (this is the **unbounded knapsack problem** because you have no limit on how many boxes you can choose).

Typically, this problem is solved using dynamic programming or math programming. If we set it up following a math program, we can write out the model as follows:

In this case, *x*i_ is can be any value ≥0 and symbolizes the number of items _i _we place into the knapsack. *v*i_ and *w*i_, are the values and weights of the items respectively.

In plain language, this small model is saying we want to maximize the value in the knapsack (which we call *z*). We do this by finding the largest number of items (*x*i_) and their values (*v*i*) without exceeding the weight limit of the knapsack (_W*). This formulation is known as an Integer Program (IP) because we have integer decision variables (we can’t pack parts of items, just full, integer values) and is solved using a solver like CPLEX, Gurobi, or GLPK (the last one is free and open source).

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Dummies guide to Reinforcement learning, Q learning, Bellman Equation. You’re getting bore stuck in lockdown, you decided to play computer games to pass your time.

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The agenda of the talk included an introduction to 3D data, its applications and case studies, 3D data alignment and more.

Most popular Data Science and Machine Learning courses — August 2020. This list was last updated in August 2020 — and will be updated regularly so as to keep it relevant

How you can use Deep Learning even for small datasets. When you’re working on Deep Learning algorithms you almost always require a large volume of data to train your model on.