def dqn_gridworld():
hp = DictConfig({})
hp.steps = 1000
hp.batch_size = 600
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(GridWorldEnvWrapper,
model,
hp,
project_name="SimpleGridWorld",
run_name="dqn")
def train_dqn_connect4():
hp = DictConfig({})
hp.steps = 20
hp.batch_size = 2
hp.max_steps = 10
hp.lr = 1e-3
hp.epsilon_exploration = 0.1
hp.gamma_discount = 0.9
model = GenericLinearModel(2 * 6 * 7, [10], 7, flatten=True).float().to(device)
train_dqn(ConnectXEnvWrapper, model, hp, name="Connect4")
def breakout_dqn():
hp = DictConfig({})
hp.steps = 2000
hp.batch_size = 32
hp.env_record_freq = 500
hp.env_record_duration = 100
hp.max_steps = 1000
hp.lr = 1e-3
hp.epsilon_exploration = 0.1
hp.gamma_discount = 0.9
model = GenericLinearModel(42 * 42 * 3, [100, 100], 4, flatten=True)
train_dqn(
BreakoutEnvWrapper, model, hp, project_name="Breakout", run_name="vanilla_dqn"
)
def __init__(self):
super().__init__()
self.env = FrozenLakeEnv(map_name="4x4", is_slippery=True)
def get_legal_actions(self):
return list(range(4))
@staticmethod
def get_state_batch(envs: Iterable) -> torch.Tensor:
return to_onehot([env.state for env in envs], 16).float()
if __name__ == "__main__":
hp = DictConfig({})
hp.steps = 5000
hp.batch_size = 500
hp.max_steps = 200
hp.lr = 1e-3
hp.epsilon_exploration = 0.1
hp.gamma_discount = 0.9
hp.units = [10]
model = GenericLinearModel(16, hp.units, 4).double().to(device)
train_dqn(FrozenLakeEnvWrapper, model, hp, name="FrozenLake")
max_steps = 500
reward_range = (-10, 10) # TODO: Fix this
def __init__(self):
super().__init__()
self.env = gym.make(
"GDY-Sokoban---2-v0",
global_observer_type=gd.ObserverType.VECTOR,
player_observer_type=gd.ObserverType.VECTOR,
level=0,
)
if __name__ == "__main__":
hp = DictConfig({})
hp.steps = 10000
hp.batch_size = 1000
hp.env_record_freq = 500
hp.env_record_duration = 50
hp.max_steps = 200
hp.lr = 1e-3
hp.epsilon_exploration = 0.1
hp.gamma_discount = 0.9
model = GenericLinearModel(5 * 7 * 8, [10], 5,
flatten=True).float().to(device)
train_dqn(SokobanV2L0EnvWrapper, model, hp, name="SokobanV2L0")
def __init__(self):
super().__init__()
self.env = gym.make("Taxi-v3")
def get_legal_actions(self):
return list(range(6))
@staticmethod
def get_state_batch(envs: Iterable) -> torch.Tensor:
return to_onehot([env.state for env in envs], 500).float()
if __name__ == "__main__":
hp = DictConfig({})
hp.steps = 10000
hp.batch_size = 500
hp.max_steps = 200
hp.lr = 1e-3
hp.epsilon_exploration = 0.1
hp.gamma_discount = 0.9
hp.units = [100]
model = GenericLinearModel(in_size=500, units=hp.units, out_size=6)
train_dqn(TaxiV3EnvWrapper, model, hp, name="TaxiV3")
from envs.env_wrapper import (
PettingZooEnvWrapper,
NumpyStateMixin,
petting_zoo_random_player,
)
from models import GenericLinearModel
from settings import device
class TicTacToeEnvWrapper(PettingZooEnvWrapper, NumpyStateMixin):
def __init__(self):
super(TicTacToeEnvWrapper, self).__init__(
env=tictactoe_v3.env(), opponent_policy=petting_zoo_random_player
)
if __name__ == "__main__":
hp = DictConfig({})
hp.steps = 20
hp.batch_size = 2
hp.max_steps = 10
hp.lr = 1e-3
hp.epsilon_exploration = 0.1
hp.gamma_discount = 0.9
model = GenericLinearModel(18, [10], 9, flatten=True).float().to(device)
train_dqn(TicTacToeEnvWrapper, model, hp, name="TicTacToe")
model = (nn.Sequential(
nn.Conv2d(3, 32, kernel_size=3, stride=2, padding=1),
nn.ELU(),
nn.Conv2d(32, 32, kernel_size=3, stride=2, padding=1),
nn.ELU(),
nn.Conv2d(32, 32, kernel_size=3, stride=2, padding=1),
nn.ELU(),
nn.Conv2d(32, 32, kernel_size=3, stride=2, padding=1),
nn.Flatten(),
nn.Linear(288, 100),
nn.ELU(),
nn.Linear(100, 12),
).float().to(device))
if __name__ == "__main__":
hp = DictConfig({})
hp.steps = 2000
hp.batch_size = 2
hp.env_record_freq = 500
hp.env_record_duration = 100
hp.max_steps = 1000
hp.lr = 1e-3
hp.epsilon_exploration = 0.1
hp.gamma_discount = 0.9
train_dqn(MarioEnvWrapper, model, hp, name="Mario")