Questions tagged [monte-carlo]

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How can I build a simulation environment that assess different risk policies? [closed]

I work in fin-tech and would like to build some sort of simulation program to assess how different inputs will impact net revenue. For example, if we create new policies based on ML scores, how would ...
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6 views

monte carlo simulation for impact to revenue by adjusting credit limits

I've been tasked with prioritizing projects for the year. One of them is to estimate the impact to net revenue if we were to adjust the purchase limits for our users. Our platform is transactional in ...
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1answer
41 views

Test data for statistical t-test in Python

first of all sorry if this is not the proper place to ask but i have been trying to create some dummy variables in order to run a students t-test as well as a welch t-test and then run a monte-carlo ...
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1answer
9 views

Stimulation model for sport

I am trying to model a stimulation for lacrosse sport, I have tried different variables but still don't get up to 50% predictions. I used scoring offence and defence, shot percentage and face off ...
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1answer
41 views

Having a reward structure which gives high positive rewards compared to the negative rewards

I am training an RL agent using PPO algorithm for a control problem. The objective of the agent is to maintain temperature in a room. It is an episodic task with episode length of 9 hrs and step size(...
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1answer
34 views

How to handle differences between training and deploying of an RL agent

Hi I am training an RL agent for a control problem. The objective of the agent is to maintain temperature in a zone. It is an episodic task with episode length of 10 hrs and actions being taken every ...
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1answer
99 views

Different results every time I train a reinforcement learning agent

I am training an RL agent for a control problem using PPO algorithm. I am using stable-baselines library for it. The objective of an agent is to maintain a temperature of 24 deg in a zone and it ...
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1answer
973 views

Evaluating a trained Reinforcement Learning Agent?

I am new to reinforcement learning agent training. I have read about PPO algorithm and used stable baselines library to train an agent using PPO. So my question here is how do I evaluate a trained RL ...
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0answers
20 views

Best Method for Data Analysis on a 100 numerical IVs and 200 numerical DVs

I think I might need the help of this valuable community for a task. I have been given a dataset for 100 numerical independent variables (IVs) that predict output for 200 numerical values (from monte ...
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1answer
58 views

How to formulate reward of an rl agent with two objectives

I have started learning reinforcement learning and trying to apply it for my use case. I am developing an rl agent which can maintain temperature at a particular value, and minimize the energy ...
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1answer
104 views

MCMC for finding Bayesian Neural Network

Is someone familiar with such an approach: Suppose I want to build a bayesian neural network, with distributions over my parameters instead of point estimates. First I train my network with standard ...
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1answer
131 views

Estimating the value of $\pi$ with a Monte Carlo dartboard: $<$ or $\leq$?

I'm trying to figure out which is the proper way to estimate $\pi$ using the Monte Carlo method randomly distributing points in a square that also contains an inscribed circle. Some sources say to ...
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1answer
32 views

What is the intuition behind using Monte Carlo to solve a differential equation

Conceptually, I understand how a numerical method like Monte Carlo is used to solve a definite integral. Because integral of a function is the area bounded by the curve, the ratio of random points ...
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1answer
49 views

Evaluating value functions in RL

I'm working my way through the book Reinforcement Learning by Richar S. Sutton and Andrew G. Barto and I am stuck on the following question. The value of a state depends on the the values of the ...
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1answer
7k views

What is Monte Carlo dropout?

I understand how to use MC dropout from this answer, but I don't understand how MC dropout works, what its purpose is, and how it differs from normal dropout.
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1answer
92 views

Why not use max(returns) instead of average(returns) in off-policy Monte Carlo control?

As I understand it, in reinforcement learning, off-policy Monte Carlo control is when the state-action value function $Q(s,a)$ is estimated as a weighted average of the observed returns. However, in ...
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1answer
25 views

In first visit monte carlo are we assuming the environment is the same over episodes?

Watching this video (11:30) that presents the simplest algorithm for reinforcement learning: Monte Carlo Policy Evaluation, which says in general: The first time a sate is visited: increment N(s): N(...
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1answer
253 views

How is Importance-Sampling Used in Off-Policy Monte Carlo Prediction?

In the section, "Off-Policy Prediction via Importance Sampling", found in the chapter on monte carlo methods of the second edition of Sutton and Barto's, "Reinforcement Learning: An Introduction", the ...
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0answers
183 views

How to use pymc3 to sample the mean of a Pareto random variable?

I Have a variable which is Pareto-ly distributed 'x', with unknown alpha and m. I want to find out the distribution of its mean, so I use the following model: ...
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1answer
968 views

Parameter estimation for a model with multiple input parameters

So I've this model that simulates an ecosystem and outputs its attributes, like its chemistry, temperature etc. There are lots of input parameters to the model. My job is to write a program to figure ...
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1answer
904 views

What visualization I should choose for Monte Carlo simulations in timeline events?

I wasn't sure if I should open this question in Cross Validated or here. But since the question belongs to a bigger project related with Data Science, I chose this one. I will present a simplified ...