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39 lines
1.1 KiB
Python
39 lines
1.1 KiB
Python
#!/usr/bin/env python
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"""Example on how to use the 'Cartpole' OpenAI Gym environments in PyRoboLearn using a linear policy trained with
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the PoWER RL algorithm.
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"""
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import matplotlib.pyplot as plt
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from pyrobolearn.envs import gym
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from pyrobolearn.policies import LinearPolicy
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from pyrobolearn.tasks import RLTask
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from pyrobolearn.algos import PoWER
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# create env, state, and action from gym
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env = gym.make('CartPole-v1')
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state, action = env.state, env.action
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print("State and action space: {} and {}".format(state.space, action.space))
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# create policy
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policy = LinearPolicy(state, action)
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# create task and run it
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task = RLTask(env, policy)
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task.run(num_steps=1000, use_terminating_condition=True, render=True)
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# create RL algo
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# Note: depends a lot on the initialization of the policy
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algo = PoWER(task, policy)
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rewards = algo.train(num_steps=1000, num_rollouts=10, num_episodes=300, verbose=True)
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# plot
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plt.figure()
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plt.plot(rewards)
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plt.show()
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# test optimized policy
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reward = algo.test(num_steps=1000, use_terminating_condition=True, render=True)
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print("Final reward obtained on the test: {}".format(reward))
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