# Path(__file__).parent / "../dataset/intersection_4lane_sv"
# ).resolve(), (
# Path(__file__).parent / "../dataset/intersection_4lane_sv_up"
# Path(__file__).parent / "../dataset_public/mixed_loop/its_merge_a"
# ).resolve(), (
# Path(__file__).parent / "../dataset/intersection_4lane_sv_right"
# Path(__file__).parent / "../dataset_public/mixed_loop/roundabout_its_a"
# ).resolve(), (
# Path(__file__).parent / "../dataset_public/mixed_loop/roundabout_merge_a"
Path(__file__).parent / "../dataset/simple"
).resolve()]
print(f"training on {scenario_paths}")
from ray.rllib.agents.trainer_template import build_trainer
from ray.rllib.agents.dqn.dqn import DEFAULT_CONFIG, DQNTrainer, validate_config, execution_plan, get_policy_class
config = DEFAULT_CONFIG.copy()
# config["seed_global"] = 0
DQN = DQNTrainer.with_updates(
name="DQN_TORCH", default_policy=DQNTorchPolicy, default_config=DEFAULT_CONFIG, get_policy_class=None)
def parse_args():
parser = argparse.ArgumentParser("train on multi scenarios")
# env setting
parser.add_argument("--scenario", type=str, default=None, help="Scenario name")
parser.add_argument("--exper", type=str, default="multi_scenarios")
parser.add_argument(
"--headless", default=False, action="store_true", help="Turn on headless mode"
)
original_traj['dones'][-1] = True # set last one to done
original_traj['rewards'] = np.array(original_traj['rewards'],
dtype=np.float)
original_traj['rewards'][-1] = \
1 - 0.9 * goal_step / self.policy.config['horizon'] # change reward
code.interact(local=locals())
except:
pass
return original_traj
# build postprocess_fn using SamplingStrategy
postprocess_with_HER = build_DQN_HER_postprocess_fn(MiniGridSamplingStrategy)
# Trainer config using Rainbow DQN with HER
HER_RAINBOW_DQN_CONFIG = DEFAULT_CONFIG.copy()
HER_RAINBOW_DQN_CONFIG.update({
# Common
"framework": "torch",
"num_gpus": 1,
# Model
# "model": {
# "dim": 5,
# "conv_filters": [
# [2, [5, 5], 1]
# ],
# "conv_activation": "relu",
# "fcnet_hiddens": [4,4],
# "max_seq_len": 100
# },
# Hindsight Experience Replay