89 lines
2.9 KiB
Python
89 lines
2.9 KiB
Python
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# Copyright 2017 The dm_control Authors.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ============================================================================
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"""Tests specific to the LQR domain."""
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from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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import math
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import unittest
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# Internal dependencies.
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from absl import logging
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from absl.testing import absltest
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from absl.testing import parameterized
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from local_dm_control_suite import lqr
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from local_dm_control_suite import lqr_solver
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import numpy as np
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from six.moves import range
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class LqrTest(parameterized.TestCase):
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@parameterized.named_parameters(
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('lqr_2_1', lqr.lqr_2_1),
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('lqr_6_2', lqr.lqr_6_2))
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def test_lqr_optimal_policy(self, make_env):
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env = make_env()
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p, k, beta = lqr_solver.solve(env)
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self.assertPolicyisOptimal(env, p, k, beta)
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@parameterized.named_parameters(
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('lqr_2_1', lqr.lqr_2_1),
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('lqr_6_2', lqr.lqr_6_2))
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@unittest.skipUnless(
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condition=lqr_solver.sp,
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reason='scipy is not available, so non-scipy DARE solver is the default.')
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def test_lqr_optimal_policy_no_scipy(self, make_env):
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env = make_env()
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old_sp = lqr_solver.sp
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try:
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lqr_solver.sp = None # Force the solver to use the non-scipy code path.
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p, k, beta = lqr_solver.solve(env)
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finally:
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lqr_solver.sp = old_sp
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self.assertPolicyisOptimal(env, p, k, beta)
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def assertPolicyisOptimal(self, env, p, k, beta):
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tolerance = 1e-3
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n_steps = int(math.ceil(math.log10(tolerance) / math.log10(beta)))
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logging.info('%d timesteps for %g convergence.', n_steps, tolerance)
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total_loss = 0.0
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timestep = env.reset()
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initial_state = np.hstack((timestep.observation['position'],
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timestep.observation['velocity']))
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logging.info('Measuring total cost over %d steps.', n_steps)
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for _ in range(n_steps):
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x = np.hstack((timestep.observation['position'],
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timestep.observation['velocity']))
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# u = k*x is the optimal policy
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u = k.dot(x)
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total_loss += 1 - (timestep.reward or 0.0)
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timestep = env.step(u)
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logging.info('Analytical expected total cost is .5*x^T*p*x.')
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expected_loss = .5 * initial_state.T.dot(p).dot(initial_state)
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logging.info('Comparing measured and predicted costs.')
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np.testing.assert_allclose(expected_loss, total_loss, rtol=tolerance)
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if __name__ == '__main__':
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absltest.main()
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