mirror of
https://github.com/Farama-Foundation/Gymnasium.git
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cdd212db4b
* Fix autodetect dtype warnings * Use warnings module for gym logger * Fix warning in tests
69 lines
2.1 KiB
Python
69 lines
2.1 KiB
Python
import numpy as np
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import gym
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from gym import spaces
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from gym.utils import seeding
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class HotterColder(gym.Env):
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"""Hotter Colder
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The goal of hotter colder is to guess closer to a randomly selected number
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After each step the agent receives an observation of:
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0 - No guess yet submitted (only after reset)
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1 - Guess is lower than the target
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2 - Guess is equal to the target
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3 - Guess is higher than the target
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The rewards is calculated as:
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(min(action, self.number) + self.range) / (max(action, self.number) + self.range)
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Ideally an agent will be able to recognise the 'scent' of a higher reward and
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increase the rate in which is guesses in that direction until the reward reaches
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its maximum
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"""
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def __init__(self):
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self.range = 1000 # +/- value the randomly select number can be between
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self.bounds = 2000 # Action space bounds
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self.action_space = spaces.Box(low=np.array([-self.bounds]), high=np.array([self.bounds]),
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dtype=np.float32)
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self.observation_space = spaces.Discrete(4)
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self.number = 0
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self.guess_count = 0
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self.guess_max = 200
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self.observation = 0
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self.seed()
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self.reset()
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def seed(self, seed=None):
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self.np_random, seed = seeding.np_random(seed)
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return [seed]
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def step(self, action):
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assert self.action_space.contains(action)
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if action < self.number:
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self.observation = 1
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elif action == self.number:
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self.observation = 2
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elif action > self.number:
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self.observation = 3
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reward = ((min(action, self.number) + self.bounds) / (max(action, self.number) + self.bounds)) ** 2
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self.guess_count += 1
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done = self.guess_count >= self.guess_max
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return self.observation, reward[0], done, {"number": self.number, "guesses": self.guess_count}
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def reset(self):
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self.number = self.np_random.uniform(-self.range, self.range)
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self.guess_count = 0
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self.observation = 0
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return self.observation
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