How to discretize continuous sensor data in OpenAI Gym environments

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1

I’m working on building a custom reinforcement learning environment using OpenAI Gym. My setup involves a temperature sensor that outputs continuous values between 50 and 150 degrees. I need to define the observation_space properly.

Right now I’m using this approach:

import numpy as np
action_choices = np.array([0, 1, 2])
temp_observations = np.arange(50, 150, 1)

This creates 100 discrete temperature values, but I’m wondering if there’s a smarter way to group these readings into bins. For example, I could create 20 bins like 50-55, 55-60, 60-65 and so on.

When I create my Q-table like this:

num_actions = action_choices.shape[0]
num_states = temp_observations.shape[0]

q_matrix = np.zeros((num_states, num_actions))
print(q_matrix)

It works but feels inefficient. What’s the recommended approach for handling continuous sensor data in RL environments? Should I stick with my current method or is there a better way to discretize the temperature range?

2

ya, binning is way smarter! using 10-20 bins is good for balancing learning speed and accuracy. np.digitize is handy too for mapping those temps correctly into bins. less states = faster training for your RL model!

3

I’ve tackled this exact problem before. Skip uniform bins like 50-55, 55-60 - adaptive binning works way better. Grab some sample data first and create bins based on your actual temperature distribution. This gives each bin roughly equal probability, which makes Q-learning perform much better.

Another trick: start with fewer bins during early training, then add more resolution once your agent learns the basics. If you’re open to it, gym.spaces.Box with policy gradient methods beats Q-tables for continuous spaces.

You’re right about state space explosion being a real problem. Dropping from 100 to 20 discrete states will speed up convergence significantly.

4

You’re on the right track with binning. I usually experiment to find the right number of bins - there’s no magic formula. I start with uniform bins for temperature control, but switch to non-uniform if certain ranges matter more for your specific use case. Try gym.spaces.Discrete(n) for your observation space once you pick your bin count. Makes it crystal clear what the environment expects. You’ll want a helper function to convert temps to bin indices too - something like temp_to_bin_index = int((temp - 50) // bin_size) works great for uniform bins. Going from 100 to 20 states will speed up convergence big time, especially early on. Just make sure your bins actually capture meaningful temperature differences for whatever you’re controlling.