def run_dqn(experiment_name):
current_dir = pathlib.Path().absolute()
directories = Save_paths(data_dir=f'{current_dir}/data', experiment_name=experiment_name)
game = Winter_is_coming(setup=PARAMS['setup'])
environment = wrappers.SinglePrecisionWrapper(game)
spec = specs.make_environment_spec(environment)
# Build the network.
def _make_network(spec) -> snt.Module:
network = snt.Sequential([
snt.Flatten(),
snt.nets.MLP([50, 50, spec.actions.num_values]),
])
tf2_utils.create_variables(network, [spec.observations])
return network
network = _make_network(spec)
# Setup the logger
if neptune_enabled:
agent_logger = NeptuneLogger(label='DQN agent', time_delta=0.1)
loop_logger = NeptuneLogger(label='Environment loop', time_delta=0.1)
PARAMS['network'] = f'{network}'
neptune.init('cvasquez/sandbox')
neptune.create_experiment(name=experiment_name, params=PARAMS)
else:
agent_logger = loggers.TerminalLogger('DQN agent', time_delta=1.)
loop_logger = loggers.TerminalLogger('Environment loop', time_delta=1.)
# Build the agent
agent = DQN(
environment_spec=spec,
network=network,
params=PARAMS,
checkpoint=True,
paths=directories,
logger=agent_logger
)
# Try running the environment loop. We have no assertions here because all
# we care about is that the agent runs without raising any errors.
loop = acme.EnvironmentLoop(environment, agent, logger=loop_logger)
loop.run(num_episodes=PARAMS['num_episodes'])
last_checkpoint_path = agent.save()
# Upload last checkpoint
if neptune_upload_checkpoint and last_checkpoint_path:
files = os.listdir(last_checkpoint_path)
for f in files:
neptune.log_artifact(os.path.join(last_checkpoint_path, f))
if neptune_enabled:
neptune.stop()
do_example_run(game,agent)
def main():
get_env_version()
cfg = DQNConfig(env="CartPole-v0", train_eps=200)
# cfg = DQNConfig(env="MountainCar-v0", train_eps=500)
get_env_information(env_name=cfg.env)
env = gym.make(cfg.env)
env.seed(0)
state_dim = env.observation_space.shape[0]
action_dim = env.action_space.n
agent = DQN(state_dim, action_dim, cfg)
rewards, smooth_rewards = train(cfg, env, agent)
os.makedirs(cfg.result_path)
agent.save(path=cfg.result_path)
save_results(rewards, smooth_rewards, tag='train', path=cfg.result_path)
plot_rewards(rewards,
smooth_rewards,
tag='train',
env=cfg.env,
algo=cfg.algo,
path=cfg.result_path)
