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import competitive_rl |
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import pytest |
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import numpy as np |
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from easydict import EasyDict |
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from dizoo.competitive_rl.envs.competitive_rl_env import CompetitiveRlEnv |
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@pytest.mark.envtest |
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class TestCompetitiveRlEnv: |
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def test_pong_single(self): |
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cfg = dict( |
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opponent_type="builtin", |
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is_evaluator=True, |
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env_id='cPongDouble-v0', |
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) |
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cfg = EasyDict(cfg) |
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env = CompetitiveRlEnv(cfg) |
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env.seed(314) |
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assert env._seed == 314 |
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obs = env.reset() |
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assert obs.shape == env.info().obs_space.shape |
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act_val = env.info().act_space.value |
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min_val, max_val = act_val['min'], act_val['max'] |
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np.random.seed(314) |
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i = 0 |
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while True: |
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random_action = np.random.randint(min_val, max_val, size=(1, )) |
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timestep = env.step(random_action) |
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if timestep.done: |
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print(timestep) |
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print('Env episode has {} steps'.format(i)) |
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break |
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assert isinstance(timestep.obs, np.ndarray) |
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assert isinstance(timestep.done, bool) |
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assert timestep.obs.shape == env.info().obs_space.shape |
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assert timestep.reward.shape == env.info().rew_space.shape |
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assert timestep.reward >= env.info().rew_space.value['min'] |
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assert timestep.reward <= env.info().rew_space.value['max'] |
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i += 1 |
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print(env.info()) |
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env.close() |
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def test_pong_double(self): |
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cfg = dict(env_id='cPongDouble-v0', ) |
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cfg = EasyDict(cfg) |
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env = CompetitiveRlEnv(cfg) |
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env.seed(314) |
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assert env._seed == 314 |
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obs = env.reset() |
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assert obs.shape == env.info().obs_space.shape |
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act_val = env.info().act_space.value |
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min_val, max_val = act_val['min'], act_val['max'] |
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np.random.seed(314) |
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i = 0 |
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while True: |
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random_action = [np.random.randint(min_val, max_val, size=(1, )) for _ in range(2)] |
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timestep = env.step(random_action) |
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if timestep.done: |
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print(timestep) |
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print('Env episode has {} steps'.format(i)) |
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break |
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assert isinstance(timestep.obs, np.ndarray) |
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assert isinstance(timestep.done, bool) |
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assert timestep.obs.shape == env.info().obs_space.shape |
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assert timestep.reward.shape == env.info().rew_space.shape |
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i += 1 |
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print(env.info()) |
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env.close() |
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