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import gym
import numpy as np
class BespokeAgent:
def __init__(self, env):
pass
def decide(self, observation):
position, velocity = observation
lb = min(-0.09*(position + 0.25) ** 2 + 0.03, 0.3*(position + 0.9)**4 - 0.008)
ub = -0.07*(position + 0.38) ** 2 + 0.07
if lb < velocity < ub:
action = 2
else:
action = 0
# print('observation: {}, lb: {}, ub: {} => action: {}'.format(observation, lb, ub, action))
return action
def learn(self, *argg):
pass
def play(i, agent, env, render=True, train=False):
episode_reward = 0
observation = env.reset()
while True:
if render:
env.render()
action = agent.decide(observation)
next_observation, reward, done, _ = env.step(action)
episode_reward += reward
if train:
agent.learn(observation, action, reward, done)
if done:
env.close()
break
observation = next_observation
print(i, episode_reward)
return i, episode_reward
if __name__ == '__main__':
env = gym.make('MountainCar-v0')
agent = BespokeAgent(env)
rewards = [play(i, agent, env) for i in range(100)]
print(rewards)
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