def setUp(self):
self.env = GymEnvironment('LunarLanderContinuous-v2')
self.env.reset()
self.parallel_env = DuplicateEnvironment([
GymEnvironment('LunarLanderContinuous-v2'),
GymEnvironment('LunarLanderContinuous-v2'),
])
self.parallel_env.reset()
def test_reset(self):
num_envs = 5
env = DuplicateEnvironment(make_vec_env(num_envs))
state = env.reset()
self.assertEqual(state.observation.shape, (num_envs, 4))
self.assertTrue((state.reward == torch.zeros(num_envs, )).all())
self.assertTrue((state.done == torch.zeros(num_envs, )).all())
self.assertTrue((state.mask == torch.ones(num_envs, )).all())
class TestContinuousPresets(unittest.TestCase):
def setUp(self):
self.env = GymEnvironment('LunarLanderContinuous-v2')
self.env.reset()
self.parallel_env = DuplicateEnvironment([
GymEnvironment('LunarLanderContinuous-v2'),
GymEnvironment('LunarLanderContinuous-v2'),
])
self.parallel_env.reset()
def tearDown(self):
if os.path.exists('test_preset.pt'):
os.remove('test_preset.pt')
def test_ddpg(self):
self.validate(ddpg)
def test_ppo(self):
self.validate(ppo)
def test_sac(self):
self.validate(sac)
def validate(self, builder):
preset = builder.device('cpu').env(self.env).build()
if isinstance(preset, ParallelPreset):
return self.validate_parallel_preset(preset)
return self.validate_standard_preset(preset)
def validate_standard_preset(self, preset):
# train agent
agent = preset.agent(writer=DummyWriter(), train_steps=100000)
agent.act(self.env.state)
# test agent
test_agent = preset.test_agent()
test_agent.act(self.env.state)
# test save/load
preset.save('test_preset.pt')
preset = torch.load('test_preset.pt')
test_agent = preset.test_agent()
test_agent.act(self.env.state)
def validate_parallel_preset(self, preset):
# train agent
agent = preset.agent(writer=DummyWriter(), train_steps=100000)
agent.act(self.parallel_env.state_array)
# test agent
test_agent = preset.test_agent()
test_agent.act(self.env.state)
# parallel test_agent
parallel_test_agent = preset.test_agent()
parallel_test_agent.act(self.parallel_env.state_array)
# test save/load
preset.save('test_preset.pt')
preset = torch.load('test_preset.pt')
test_agent = preset.test_agent()
test_agent.act(self.env.state)
def test_step_until_done(self):
num_envs = 3
env = DuplicateEnvironment(make_vec_env(num_envs))
env.seed(5)
env.reset()
for _ in range(100):
state = env.step(torch.ones(num_envs, dtype=torch.int32))
if state.done[0]:
break
self.assertEqual(state[0].observation.shape, (4, ))
self.assertEqual(state[0].reward, 1.)
self.assertTrue(state[0].done)
self.assertEqual(state[0].mask, 0)
def test_same_as_duplicate(self):
n_envs = 3
torch.manual_seed(42)
env1 = DuplicateEnvironment([GymEnvironment('CartPole-v0') for i in range(n_envs)])
env2 = GymVectorEnvironment(make_vec_env(n_envs), "CartPole-v0")
env1.seed(42)
env2.seed(42)
state1 = env1.reset()
state2 = env2.reset()
self.assertEqual(env1.name, env2.name)
self.assertEqual(env1.action_space.n, env2.action_space.n)
self.assertEqual(env1.observation_space.shape, env2.observation_space.shape)
self.assertEqual(env1.num_envs, 3)
self.assertEqual(env2.num_envs, 3)
act_space = env1.action_space
for i in range(2):
self.assertTrue(torch.all(torch.eq(state1.observation, state2.observation)))
self.assertTrue(torch.all(torch.eq(state1.reward, state2.reward)))
self.assertTrue(torch.all(torch.eq(state1.done, state2.done)))
self.assertTrue(torch.all(torch.eq(state1.mask, state2.mask)))
actions = torch.tensor([act_space.sample() for i in range(n_envs)])
state1 = env1.step(actions)
state2 = env2.step(actions)
def setUp(self):
self.env = AtariEnvironment('Breakout')
self.env.reset()
self.parallel_env = DuplicateEnvironment([AtariEnvironment('Breakout'), AtariEnvironment('Breakout')])
self.parallel_env.reset()
class TestAtariPresets(unittest.TestCase):
def setUp(self):
self.env = AtariEnvironment('Breakout')
self.env.reset()
self.parallel_env = DuplicateEnvironment([AtariEnvironment('Breakout'), AtariEnvironment('Breakout')])
self.parallel_env.reset()
def tearDown(self):
if os.path.exists('test_preset.pt'):
os.remove('test_preset.pt')
def test_a2c(self):
self.validate_preset(a2c)
def test_c51(self):
self.validate_preset(c51)
def test_ddqn(self):
self.validate_preset(ddqn)
def test_dqn(self):
self.validate_preset(dqn)
def test_ppo(self):
self.validate_preset(ppo)
def test_rainbow(self):
self.validate_preset(rainbow)
def test_vac(self):
self.validate_preset(vac)
def test_vpq(self):
self.validate_preset(vpg)
def test_vsarsa(self):
self.validate_preset(vsarsa)
def test_vqn(self):
self.validate_preset(vqn)
def validate_preset(self, builder):
preset = builder.device('cpu').env(self.env).build()
if isinstance(preset, ParallelPreset):
return self.validate_parallel_preset(preset)
return self.validate_standard_preset(preset)
def validate_standard_preset(self, preset):
# train agent
agent = preset.agent(writer=DummyWriter(), train_steps=100000)
agent.act(self.env.state)
# test agent
test_agent = preset.test_agent()
test_agent.act(self.env.state)
# test save/load
preset.save('test_preset.pt')
preset = torch.load('test_preset.pt')
test_agent = preset.test_agent()
test_agent.act(self.env.state)
def validate_parallel_preset(self, preset):
# train agent
agent = preset.agent(writer=DummyWriter(), train_steps=100000)
agent.act(self.parallel_env.state_array)
# test agent
test_agent = preset.test_agent()
test_agent.act(self.env.state)
# parallel test_agent
parallel_test_agent = preset.test_agent()
parallel_test_agent.act(self.parallel_env.state_array)
# test save/load
preset.save('test_preset.pt')
preset = torch.load('test_preset.pt')
test_agent = preset.test_agent()
test_agent.act(self.env.state)
def test_num_envs(self):
num_envs = 5
env = DuplicateEnvironment(make_vec_env(num_envs))
self.assertEqual(env.num_envs, num_envs)
self.assertEqual((num_envs, ), env.reset().shape)
def test_env_name(self):
env = DuplicateEnvironment(make_vec_env())
self.assertEqual(env.name, 'CartPole-v0')
def setUp(self):
self.env = GymEnvironment('CartPole-v0')
self.env.reset()
self.parallel_env = DuplicateEnvironment([GymEnvironment('CartPole-v0'), GymEnvironment('CartPole-v0')])
self.parallel_env.reset()