47 lines
870 B
Python
47 lines
870 B
Python
# %%
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from stable_baselines3 import PPO
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from intersim.envs.intersimple import NRasterized, speed_reward
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import functools
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model_name = "ppo_speed_image"
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#def reward(state, action, info):
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# speed = state[2].item()
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# r = speed if speed < 10 else (10 - 5 * (speed - 10))
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# return 0.1 * r
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env = NRasterized(
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agent=20,
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reward=functools.partial(
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speed_reward,
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collision_penalty=0
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),
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)
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# %%
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model = PPO(
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"CnnPolicy", env,
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verbose=1,
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)
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model.learn(total_timesteps=100000)
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model.save(model_name)
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print('Done training.')
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del model # remove to demonstrate saving and loading
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# %%
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model = PPO.load(model_name)
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obs = env.reset()
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while True:
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action, _states = model.predict(obs)
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obs, rewards, done, info = env.step(action)
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env.render(mode='post')
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if done:
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break
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env.close(filestr='render/'+model_name)
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# %%
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