class TestMultiagentAtariPresets(unittest.TestCase):
def setUp(self):
self.env = MultiagentAtariEnv('pong_v2', device='cpu')
self.env.reset()
def tearDown(self):
if os.path.exists('test_preset.pt'):
os.remove('test_preset.pt')
def test_independent(self):
env = MultiagentAtariEnv('pong_v2', device='cpu')
presets = {
agent_id: dqn.device('cpu').env(env.subenvs[agent_id]).build()
for agent_id in env.agents
}
self.validate_preset(
IndependentMultiagentPreset('independent', 'cpu', presets), env)
def validate_preset(self, preset, env):
# normal agent
agent = preset.agent(writer=DummyWriter(), train_steps=100000)
agent.act(self.env.last())
# test agent
test_agent = preset.test_agent()
test_agent.act(self.env.last())
# test save/load
preset.save('test_preset.pt')
preset = torch.load('test_preset.pt')
test_agent = preset.test_agent()
test_agent.act(self.env.last())
def test_reset(self):
env = MultiagentAtariEnv('pong_v1', device='cpu')
state = env.reset()
self.assertEqual(state.observation.shape, (1, 84, 84))
self.assertEqual(state.reward, 0)
self.assertEqual(state.done, False)
self.assertEqual(state.mask, 1.)
self.assertEqual(state['agent'], 'first_0')
def test_step_tensor(self):
env = MultiagentAtariEnv('pong_v1', device='cpu')
env.reset()
state = env.step(torch.tensor([0]))
self.assertEqual(state.observation.shape, (1, 84, 84))
self.assertEqual(state.reward, 0)
self.assertEqual(state.done, False)
self.assertEqual(state.mask, 1.)
self.assertEqual(state['agent'], 'second_0')
def test_independent_cuda(self):
env = MultiagentAtariEnv('pong_v2', max_cycles=1000, device=CUDA)
presets = {
agent_id: dqn.device(CUDA).env(env.subenvs[agent_id]).build()
for agent_id in env.agents
}
validate_multiagent(IndependentMultiagentPreset('independent', CUDA, presets), env)
def test_independent(self):
env = MultiagentAtariEnv('pong_v1', device='cpu')
presets = {
agent_id: dqn.device('cpu').env(env.subenvs[agent_id]).build()
for agent_id in env.agents
}
self.validate_preset(IndependentMultiagentPreset('independent', 'cpu', presets), env)
def main():
parser = argparse.ArgumentParser(description="Run an multiagent Atari benchmark.")
parser.add_argument("env", help="Name of the Atari game (e.g. Pong).")
parser.add_argument(
"agent", help="Name of the agent (e.g. dqn). See presets for available agents."
)
parser.add_argument(
"--device",
default="cuda",
help="The name of the device to run the agent on (e.g. cpu, cuda, cuda:0).",
)
parser.add_argument(
"--frames", type=int, default=40e6, help="The number of training frames."
)
parser.add_argument(
"--render", type=bool, default=False, help="Render the environment."
)
parser.add_argument(
"--writer", default='tensorboard', help="The backend used for tracking experiment metrics."
)
args = parser.parse_args()
env = MultiagentAtariEnv(args.env, device=args.device)
agent_name = args.agent
agent = getattr(multiagent_atari, agent_name)
experiment = MultiagentEnvExperiment(agent(device=args.device), env, write_loss=False, writer=args.writer)
experiment.train(frames=args.frames)
def main():
parser = argparse.ArgumentParser(
description="Watch pretrained multiagent atari")
parser.add_argument("env", help="Name of the Atari game (e.g. pong-v1)")
parser.add_argument("filename", help="File where the model was saved.")
parser.add_argument(
"--device",
default="cuda",
help=
"The name of the device to run the agent on (e.g. cpu, cuda, cuda:0)",
)
parser.add_argument(
"--fps",
default=30,
type=int,
help="Playback speed",
)
parser.add_argument("--reload",
action="store_true",
default=False,
help="Reload the model from disk after every episode")
args = parser.parse_args()
env = MultiagentAtariEnv(args.env, device=args.device)
watch(env, args.filename, args.fps, args.reload)
def test_is_done(self):
env = MultiagentAtariEnv('pong_v1', device='cpu')
env.reset()
self.assertFalse(env.is_done('first_0'))
self.assertFalse(env.is_done('second_0'))
def test_list_agents(self):
env = MultiagentAtariEnv('pong_v1', device='cpu')
self.assertEqual(env.agents, ['first_0', 'second_0'])
def test_init(self):
MultiagentAtariEnv('pong_v1', device='cpu')
MultiagentAtariEnv('mario_bros_v2', device='cpu')
MultiagentAtariEnv('entombed_cooperative_v2', device='cpu')
def test_action_spaces(self):
action_spaces = MultiagentAtariEnv('pong_v1',
device='cpu').action_spaces
self.assertEqual(action_spaces['first_0'].n, 18)
self.assertEqual(action_spaces['second_0'].n, 18)
def test_state_spaces(self):
state_spaces = MultiagentAtariEnv('pong_v1', device='cpu').state_spaces
self.assertEqual(state_spaces['first_0'].shape, (1, 84, 84))
self.assertEqual(state_spaces['second_0'].shape, (1, 84, 84))
def setUp(self):
np.random.seed(0)
torch.manual_seed(0)
self.env = MultiagentAtariEnv('space_invaders_v1', device='cpu')
self.env.seed(0)
self.experiment = None
def test_name(self):
env = MultiagentAtariEnv('pong_v1', device='cpu')
self.assertEqual(env.name, 'pong_v1')
def setUp(self):
self.env = MultiagentAtariEnv('pong_v2', device='cpu')
self.env.reset()
def test_agent_iter(self):
env = MultiagentAtariEnv('pong_v1', device='cpu')
env.reset()
it = iter(env.agent_iter())
self.assertEqual(next(it), 'first_0')
class TestMultiagentEnvExperiment(unittest.TestCase):
def setUp(self):
np.random.seed(0)
torch.manual_seed(0)
self.env = MultiagentAtariEnv('space_invaders_v1', device='cpu')
self.env.seed(0)
self.experiment = None
def test_adds_default_name(self):
experiment = MockExperiment(self.make_preset(),
self.env,
quiet=True,
save_freq=float('inf'))
self.assertEqual(experiment._writer.label,
"independent_space_invaders_v1")
def test_adds_custom_name(self):
experiment = MockExperiment(self.make_preset(),
self.env,
name='custom',
quiet=True,
save_freq=float('inf'))
self.assertEqual(experiment._writer.label, "custom_space_invaders_v1")
def test_writes_training_returns(self):
experiment = MockExperiment(self.make_preset(),
self.env,
quiet=True,
save_freq=float('inf'))
experiment.train(episodes=3)
self.assertEqual(
experiment._writer.data, {
'evaluation/first_0/returns/frame': {
'values': [465.0, 235.0, 735.0, 415.0],
'steps': [766, 1524, 2440, 3038]
},
'evaluation/second_0/returns/frame': {
'values': [235.0, 465.0, 170.0, 295.0],
'steps': [766, 1524, 2440, 3038]
}
})
def test_writes_test_returns(self):
experiment = MockExperiment(self.make_preset(),
self.env,
quiet=True,
save_freq=float('inf'))
experiment.train(episodes=3)
experiment._writer.data = {}
experiment.test(episodes=3)
self.assertEqual(list(experiment._writer.data.keys()), [
'evaluation/first_0/returns-test/mean',
'evaluation/first_0/returns-test/std',
'evaluation/second_0/returns-test/mean',
'evaluation/second_0/returns-test/std'
])
steps = experiment._writer.data[
'evaluation/first_0/returns-test/mean']['steps'][0]
for datum in experiment._writer.data.values():
self.assertEqual(len(datum['values']), 1)
self.assertGreaterEqual(datum['values'][0], 0.0)
self.assertEqual(len(datum['steps']), 1)
self.assertEqual(datum['steps'][0], steps)
def test_writes_loss(self):
experiment = MockExperiment(self.make_preset(),
self.env,
quiet=True,
write_loss=True,
save_freq=float('inf'))
self.assertTrue(experiment._writer.write_loss)
experiment = MockExperiment(self.make_preset(),
self.env,
quiet=True,
write_loss=False,
save_freq=float('inf'))
self.assertFalse(experiment._writer.write_loss)
def make_preset(self):
return IndependentMultiagentPreset(
'independent', 'cpu', {
agent: dqn.device('cpu').env(env).build()
for agent, env in self.env.subenvs.items()
})
