new_height=84,
to_gray=True,
noop_action=[1, 0, 0, 0],
start_action=[0, 1, 0, 0],
clip_rewards=True)
test_env = AtariWrapper(env_name,
action_repeat=4,
obs_stack=4,
new_width=84,
new_height=84,
to_gray=True,
start_action=[0, 1, 0, 0])
agent = DeepQ(env=env,
use_double=True,
model=DeepQModel(nature_arch=True, dueling=False),
optimizer=RMSProp(7e-4, decay=0.99, epsilon=0.1),
policy=EGreedyPolicy(1.0, 0.1, 4000000),
targetfreq=10000)
trainer = ReplayTrainer(env=env,
agent=agent,
maxsteps=80000000,
replay=backPropagationReplay,
logdir='tmp/%s/moving_average_bias' % env_name,
logfreq=900,
test_env=test_env,
test_render=False)
trainer.train()
except ImportError:
import os.path
import sys
sys.path.append(os.path.join(os.path.dirname(__file__), '..', '..'))
import reinforceflow
from reinforceflow.agents import DeepQ
from reinforceflow.envs import Vectorize
from reinforceflow.core import EGreedyPolicy, ProportionalReplay
from reinforceflow.core import Adam
from reinforceflow.models import FullyConnected
from reinforceflow.trainers.replay_trainer import ReplayTrainer
reinforceflow.set_random_seed(555)
env_name = 'CartPole-v0'
env = Vectorize(env_name)
policy = EGreedyPolicy(eps_start=1.0, eps_final=0.2, anneal_steps=300000)
agent = DeepQ(env,
model=FullyConnected(),
optimizer=Adam(0.0001),
targetfreq=10000,
policy=EGreedyPolicy(1, 0.4, 300000))
trainer = ReplayTrainer(env=env,
agent=agent,
maxsteps=300000,
replay=ProportionalReplay(30000, 32, 32),
logdir='/tmp/rf/DeepQ/%s' % env_name,
logfreq=10)
trainer.train()
new_height=84,
to_gray=True,
noop_action=[1, 0, 0, 0],
start_action=[0, 1, 0, 0],
clip_rewards=True)
test_env = AtariWrapper(env_name,
action_repeat=4,
obs_stack=4,
new_width=84,
new_height=84,
to_gray=True,
start_action=[0, 1, 0, 0])
agent = DeepQ(env=env,
use_double=True,
model=DeepQModel(nature_arch=True, dueling=False),
optimizer=RMSProp(7e-4, decay=0.99, epsilon=0.1),
policy=EGreedyPolicy(1.0, 0.1, 4000000),
targetfreq=10000)
trainer = ReplayTrainer(env=env,
agent=agent,
maxsteps=80000000,
replay=backPropagationReplay,
logdir='tmp/%s/backpropagation' % env_name,
logfreq=1800,
test_env=test_env,
test_render=False)
trainer.train()
env_name = 'CartPole-v0'
env = Vectorize(env_name)
policy = EGreedyPolicy(eps_start=1.0, eps_final=0.2, anneal_steps=300000)
agent = DeepQ(env,
device='/cpu:0',
model=FullyConnected(),
optimizer=Adam(0.0001),
targetfreq=10000,
policy=EGreedyPolicy(1, 0.4, 300000))
backPropagationReplay = BackPropagationReplay(30000,
32,
0.,
moving_average_accumulator,
32,
beta=20)
windowedPropagationReplay = WindowedBackPropagationReplay(30000, 32, 100, 32)
trainer = ReplayTrainer(env=env,
agent=agent,
maxsteps=300000,
replay=windowedPropagationReplay,
logdir='tmp/rf/DeepQ/%s' % env_name,
logfreq=10)
if __name__ == '__main__':
trainer.train()
new_height=84,
to_gray=True,
noop_action=[1, 0, 0, 0],
start_action=[0, 1, 0, 0],
clip_rewards=True)
test_env = AtariWrapper(env_name,
action_repeat=4,
obs_stack=4,
new_width=84,
new_height=84,
to_gray=True,
start_action=[0, 1, 0, 0])
agent = DeepQ(env,
use_double=True,
model=DeepQModel(nature_arch=True, dueling=False),
optimizer=RMSProp(7e-4, decay=0.99, epsilon=0.1),
policy=EGreedyPolicy(1.0, 0.1, 4000000),
targetfreq=10000)
trainer = ReplayTrainer(env=env,
agent=agent,
maxsteps=80000000,
replay=ProportionalReplay(400000, 32, 32),
logdir='tmp/%s/proportional2' % env_name,
logfreq=1800,
test_env=test_env,
test_render=False)
trainer.train()
clip_rewards=True)
test_env = AtariWrapper(env_name,
action_repeat=4,
obs_stack=4,
new_width=84,
new_height=84,
to_gray=True,
start_action=[0, 1, 0, 0])
agent = DeepQ(env=env,
model=DeepQModel(nature_arch=True, dueling=False),
use_double = True,
restore_from = None,
optimizer = RMSProp(7e-4, decay=0.99, epsilon=0.1),
policy = EGreedyPolicy(1.0, 0.1, 4000000),
targetfreq = 10000,
)
trainer = ReplayTrainer(env=env,
agent=agent,
maxsteps=80000000,
replay = ExperienceReplay(400000,32,32),
logdir='tmp/%s/vanilla' % env_name,
logfreq=1800,
render = False,
test_env = test_env,
test_render = False,
)
trainer.train()