def dqn_per_gridworld():
hp = DictConfig({})
hp.steps = 1000
hp.batch_size = 500
hp.replay_batch = 100
hp.replay_size = 1000
hp.delete_freq = 100 * (hp.batch_size + hp.replay_size) # every 100 steps
hp.env_record_freq = 100
hp.env_record_duration = 25
hp.max_steps = 50
hp.grid_size = 4
hp.lr = 1e-3
hp.epsilon_exploration = 0.1
hp.gamma_discount = 0.9
model = (GenericConvModel(height=4,
width=4,
in_channels=4,
channels=[50],
out_size=4).float().to(device))
train_dqn_per(
GridWorldEnvWrapper,
model,
hp,
project_name="SimpleGridWorld",
run_name="dqn_per",
)
def breakout_double_dqn():
hp = DictConfig({})
hp.steps = 2000
hp.batch_size = 50
hp.replay_batch = 50
hp.replay_size = 1000
hp.delete_freq = 50 * (hp.batch_size + hp.replay_size) # every 100 steps
hp.delete_percentage = 0.2
hp.env_record_freq = 100
hp.env_record_duration = 50
hp.lr = 1e-3
hp.gamma_discount = 0.9
# hp.epsilon_exploration = 0.1
hp.epsilon_flatten_step = 1500
hp.epsilon_start = 1
hp.epsilon_end = 0.1
hp.epsilon_decay_function = decay_functions.LINEAR
hp.target_model_sync_freq = 50
model = GenericConvModel(42, 42, 3, [50, 50, 50], [100], 4)
train_dqn_double(
BreakoutEnvWrapper, model, hp, project_name="Breakout", run_name="double_dqn"
)
def dqn_double():
hp = DictConfig({})
hp.steps = 1000
hp.batch_size = 500
hp.replay_batch = 100
hp.replay_size = 1000
hp.delete_freq = 100 * (hp.batch_size + hp.replay_size) # every 100 steps
hp.env_record_freq = 100
hp.env_record_duration = 25
hp.max_steps = 50
hp.grid_size = 4
hp.lr = 1e-3
hp.gamma_discount = 0.9
# hp.epsilon_exploration = 0.1
hp.epsilon_flatten_step = 700
hp.epsilon_start = 1
hp.epsilon_end = 0.001
hp.epsilon_decay_function = decay_functions.LINEAR
hp.target_model_sync_freq = 50
model = (GenericConvModel(height=4,
width=4,
in_channels=4,
channels=[50],
out_size=4).float().to(device))
train_dqn_double(
GridWorldEnvWrapper,
model,
hp,
project_name="SimpleGridWorld",
run_name="dqn_target",
)
