Model Overview
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import asyncio | |
import concurrent.futures | |
import requests | |
Seconds = [ | |
("first", 5), | |
("second", 0), | |
("third", 3) | |
] |
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import os | |
import random | |
import gym | |
class Hanamichi(): | |
def __init__(self): | |
self.actions = [2, 3] # up and down | |
self.fun = [] |
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dummy gist for image |
Solve CartPole environment on OpenAI Gym by simple q-learning algorithm
Model Overview
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dummy for images |
Solve CartPole environment on OpenAI Gym by simple q-learning algorithm (Parameter tuned version)
- gamma: 0.99
- bin size: [3, 3, 8, 5]
- low bound: [None, -0.5, None, -math.radians(50)]
- high bound: [None, 0.5, None, math.radians(50)]
- learning rate update rule: max(0.1, min(0.5, 1.0 - math.log10((t + 1) / 25)))
- epsilon update rule: max(0.01, min(1.0, 1.0 - math.log10((t + 1) / 25)))
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print("various fonts") |
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circle-cut-ideas |
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dummy |