def test_ddpg_create_ddpg_model(model_cls, s_format, env):
data = MDPDataBunch.from_env(env,
render='rgb_array',
bs=32,
add_valid=False,
feed_type=s_format)
model = create_ddpg_model(data, model_cls)
model.eval()
model(data.state.s.float())
check_shape(env, data, s_format)
data.close()
def test_ddpg_ddpglearner(model_cls, s_format, mem, env):
data = MDPDataBunch.from_env(env,
render='rgb_array',
bs=32,
add_valid=False,
feed_type=s_format)
model = create_ddpg_model(data, model_cls)
memory = mem(memory_size=1000, reduce_ram=True)
exploration_method = OrnsteinUhlenbeck(size=data.action.taken_action.shape,
epsilon_start=1,
epsilon_end=0.1,
decay=0.001)
ddpg_learner(data=data,
model=model,
memory=memory,
exploration_method=exploration_method)
check_shape(env, data, s_format)
data.close()
def test_ddpg_models_mountain_car_continuous(model_cls, s_format, experience):
group_interp = GroupAgentInterpretation()
for i in range(5):
print('\n')
data = MDPDataBunch.from_env(
'MountainCarContinuous-v0',
render='rgb_array',
bs=40,
add_valid=False,
keep_env_open=False,
feed_type=s_format,
memory_management_strategy='k_partitions_top',
k=3,
res_wrap=partial(ResolutionWrapper, w_step=2, h_step=2))
exploration_method = OrnsteinUhlenbeck(
size=data.action.taken_action.shape,
epsilon_start=1,
epsilon_end=0.1,
decay=0.0001)
memory = experience(memory_size=1000000, reduce_ram=True)
model = create_ddpg_model(data=data, base_arch=model_cls)
learner = ddpg_learner(data=data,
model=model,
memory=memory,
exploration_method=exploration_method,
callback_fns=[RewardMetric, EpsilonMetric])
learner.fit(450)
meta = f'{experience.__name__}_{"FEED_TYPE_STATE" if s_format==FEED_TYPE_STATE else "FEED_TYPE_IMAGE"}'
interp = AgentInterpretation(learner, ds_type=DatasetType.Train)
interp.plot_rewards(cumulative=True, per_episode=True, group_name=meta)
group_interp.add_interpretation(interp)
group_interp.to_pickle(
f'../docs_src/data/mountaincarcontinuous_{model.name.lower()}/',
f'{model.name.lower()}_{meta}')
[
g.write('../res/run_gifs/mountaincarcontinuous')
for g in interp.generate_gif()
]
data.close()
del learner
del model
del data
def trained_learner(model_cls,
env,
s_format,
experience,
bs=64,
layers=None,
render='rgb_array',
memory_size=1000000,
decay=0.0001,
lr=None,
actor_lr=None,
epochs=450,
opt=torch.optim.RMSprop,
**kwargs):
lr, actor_lr = ifnone(lr, 1e-3), ifnone(actor_lr, 1e-4)
data = MDPDataBunch.from_env(env,
render=render,
bs=bs,
add_valid=False,
keep_env_open=False,
feed_type=s_format,
memory_management_strategy='k_partitions_top',
k=3,
**kwargs)
exploration_method = OrnsteinUhlenbeck(size=data.action.taken_action.shape,
epsilon_start=1,
epsilon_end=0.1,
decay=decay)
memory = experience(memory_size=memory_size, reduce_ram=True)
model = create_ddpg_model(data=data,
base_arch=model_cls,
lr=lr,
actor_lr=actor_lr,
layers=layers,
opt=opt)
learner = ddpg_learner(data=data,
model=model,
memory=memory,
exploration_method=exploration_method,
callback_fns=[RewardMetric, EpsilonMetric])
learner.fit(epochs)
return learner