def create_agent(self, env):
model = agents.ddpg.DDPGModel(
policy=create_deterministic_policy_for_env(env),
q_func=create_state_action_q_function_for_env(env))
rbuf = replay_buffer.ReplayBuffer(10 ** 5)
opt_a = optimizers.Adam()
opt_a.setup(model.policy)
opt_b = optimizers.Adam()
opt_b.setup(model.q_function)
explorer = explorers.AdditiveGaussian(scale=1)
return agents.DDPG(model, opt_a, opt_b, rbuf, gamma=0.99,
explorer=explorer)
def _test_load_ddpg(self, gpu):
def concat_obs_and_action(obs, action):
return F.concat((obs, action), axis=-1)
action_size = 3
winit = chainer.initializers.LeCunUniform(3**-0.5)
q_func = chainer.Sequential(
concat_obs_and_action,
L.Linear(None, 400, initialW=winit),
F.relu,
L.Linear(None, 300, initialW=winit),
F.relu,
L.Linear(None, 1, initialW=winit),
)
policy = chainer.Sequential(
L.Linear(None, 400, initialW=winit),
F.relu,
L.Linear(None, 300, initialW=winit),
F.relu,
L.Linear(None, action_size, initialW=winit),
F.tanh,
chainerrl.distribution.ContinuousDeterministicDistribution,
)
from chainerrl.agents.ddpg import DDPGModel
model = DDPGModel(q_func=q_func, policy=policy)
obs_low = [-np.inf] * 11
fake_obs = chainer.Variable(model.xp.zeros_like(
obs_low, dtype=np.float32)[None],
name='observation')
fake_action = chainer.Variable(model.xp.zeros_like(
[-1., -1., -1.], dtype=np.float32)[None],
name='action')
policy(fake_obs)
q_func(fake_obs, fake_action)
opt_a = optimizers.Adam()
opt_c = optimizers.Adam()
opt_a.setup(model['policy'])
opt_c.setup(model['q_function'])
explorer = explorers.AdditiveGaussian(scale=0.1,
low=[-1., -1., -1.],
high=[1., 1., 1.])
agent = agents.DDPG(model,
opt_a,
opt_c,
replay_buffer.ReplayBuffer(100),
gamma=0.99,
explorer=explorer,
replay_start_size=1000,
target_update_method='soft',
target_update_interval=1,
update_interval=1,
soft_update_tau=5e-3,
n_times_update=1,
gpu=gpu,
minibatch_size=100,
burnin_action_func=None)
model, exists = download_model("DDPG",
"Hopper-v2",
model_type=self.pretrained_type)
agent.load(model)
if os.environ.get('CHAINERRL_ASSERT_DOWNLOADED_MODEL_IS_CACHED'):
assert exists