def main():
parsed_args = parse_command_line_args(sys.argv[1:])
num_last_frames = 4
env = create_snake_environment(parsed_args.level)
model = nn.Sequential(
nn.Conv2d(num_last_frames, 16, 3),
nn.ReLU(),
nn.Conv2d(16, 32, 3),
nn.ReLU(),
nn.Flatten(),
nn.Linear(1152, 256),
nn.ReLU(),
nn.Linear(256, env.num_actions),
)
agent = DeepQNetworkAgent(
model=model,
env_shape=env.observation_shape,
num_actions=env.num_actions,
memory_size=-1,
num_last_frames=num_last_frames,
)
agent.train(
env,
batch_size=64,
num_episodes=parsed_args.num_episodes,
checkpoint_freq=parsed_args.num_episodes // 10,
discount_factor=0.95,
)
def main():
parsed_args = parse_command_line_args(sys.argv[1:])
env = create_snake_environment(parsed_args.level)
model = create_dqn_model(env, num_last_frames=4)
agent = DeepQNetworkAgent(model=model,
memory_size=-1,
num_last_frames=model.input_shape[1])
agent.train(env,
batch_size=64,
num_episodes=parsed_args.num_episodes,
checkpoint_freq=parsed_args.num_episodes // 10,
discount_factor=0.95)
def create_agent(name, model):
"""
Create a specific type of Snake AI agent.
Args:
name (str): key identifying the agent type.
model: (optional) a pre-trained model required by certain agents.
Returns:
An instance of Snake agent.
"""
from snakeai.agent import DeepQNetworkAgent, HumanAgent, RandomActionAgent
if name == 'human':
return HumanAgent()
elif name == 'dqn':
if model is None:
raise ValueError('A model file is required for a DQN agent.')
return DeepQNetworkAgent(model=model,
memory_size=-1,
num_last_frames=4)
elif name == 'random':
return RandomActionAgent()
raise KeyError(f'Unknown agent type: "{name}"')
def create_agent(name, model, env):
"""
Create a specific type of Snake AI agent.
Args:
name (str): key identifying the agent type.
model: (optional) a pre-trained model required by certain agents.
env: an instance of Snake environment.
Returns:
An instance of Snake agent.
"""
from snakeai.agent import DeepQNetworkAgent, HumanAgent, RandomActionAgent
if name == "human":
return HumanAgent()
elif name == "dqn":
if model is None:
raise ValueError("A model file is required for a DQN agent.")
return DeepQNetworkAgent(
model=model,
memory_size=-1,
num_last_frames=4,
env_shape=env.observation_shape,
num_actions=env.num_actions,
)
elif name == "random":
return RandomActionAgent()
raise KeyError(f'Unknown agent type: "{name}"')
def main():
# Handle input params. load parsed args if specified by users
# otherwise load from config file
parsed_args = parse_command_line_args(sys.argv[1:])
level = parsed_args.level if parsed_args.level else Config.LEVEL
num_episodes = parsed_args.num_episodes if parsed_args.num_episodes else Config.NUM_EPISODES
# Create a folder for data output.
timestamp = time.strftime('%Y%m%d-%H%M%S')
output_path = os.path.join(
'outputs',
str(timestamp) + '_' + Config.LEARNING_METHOD + '_' +
str(num_episodes) + 'epsiodes_' + os.path.basename(level))
os.makedirs(output_path)
# dump a copy of config and env to outputs
copy(level, output_path)
copy("config.py", output_path)
env = create_snake_environment(level, output_path)
model = []
if parsed_args.model:
model.append(load_model(parsed_args.model))
model.append(load_model(parsed_args.model))
elif Config.USE_PRETRAINED_MODEL:
model.append(load_model(Config.PRETRAINED_MODEL))
model.append(load_model(Config.PRETRAINED_MODEL))
else:
model.append(
create_dqn_model(env, num_last_frames=Config.NUM_LAST_FRAMES))
model.append(
create_dqn_model(env, num_last_frames=Config.NUM_LAST_FRAMES))
agent = DeepQNetworkAgent(model=model,
memory_size=Config.MEMORY_SIZE,
num_last_frames=model[0].input_shape[1],
output=output_path)
agent.train(env,
batch_size=Config.BATCH_SIZE,
num_episodes=num_episodes,
checkpoint_freq=100,
discount_factor=Config.DISCOUNT_FACTOR,
method=Config.LEARNING_METHOD,
multi_step=Config.MULTI_STEP_REWARD)
def main():
# Create a folder for data output.
timestamp = time.strftime('%Y%m%d-%H%M%S')
output_path = os.path.join('outputs', str(timestamp))
os.makedirs(output_path)
parsed_args = parse_command_line_args(sys.argv[1:])
env = create_snake_environment(parsed_args.level, output_path)
model = create_dqn_model(env, num_last_frames=4)
agent = DeepQNetworkAgent(model=model,
memory_size=-1,
num_last_frames=model.input_shape[1],
output=output_path)
agent.train(env,
batch_size=64,
num_episodes=parsed_args.num_episodes,
checkpoint_freq=parsed_args.num_episodes // 10,
discount_factor=0.95)
def main():
parsed_args = parse_command_line_args(sys.argv[1:])
env = create_snake_environment(parsed_args.level)
if parsed_args.attention == -1:
model = create_dqn_model(env, num_last_frames=4)
else:
model = create_vin_model(env,
num_last_frames=4,
attention=parsed_args.attention)
#model.load_weights('dqn-final.model')
agent = DeepQNetworkAgent(model=model,
memory_size=-1,
num_last_frames=4,
attention=parsed_args.attention)
agent.train(env,
batch_size=64,
num_episodes=parsed_args.num_episodes,
checkpoint_freq=parsed_args.num_episodes // 10,
discount_factor=0.95)
'''agent.train(