File size: 3,962 Bytes
079c32c |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 |
from easydict import EasyDict
nstep = 1
lunarlander_dqn_gail_config = dict(
exp_name='lunarlander_dqn_gail_seed0',
env=dict(
# Whether to use shared memory. Only effective if "env_manager_type" is 'subprocess'
# Env number respectively for collector and evaluator.
collector_env_num=8,
evaluator_env_num=8,
env_id='LunarLander-v2',
n_evaluator_episode=8,
stop_value=200,
),
reward_model=dict(
type='gail',
input_size=9,
hidden_size=64,
batch_size=64,
learning_rate=1e-3,
update_per_collect=100,
collect_count=100000,
# Users should add their own model path here. Model path should lead to a model.
# Absolute path is recommended.
# In DI-engine, it is ``exp_name/ckpt/ckpt_best.pth.tar``.
expert_model_path='model_path_placeholder',
# Path where to store the reward model
reward_model_path='data_path_placeholder+/reward_model/ckpt/ckpt_best.pth.tar',
# Users should add their own data path here. Data path should lead to a file to store data or load the stored data.
# Absolute path is recommended.
# In DI-engine, it is usually located in ``exp_name`` directory
# e.g. 'exp_name/expert_data.pkl'
data_path='data_path_placeholder',
),
policy=dict(
# Whether to use cuda for network.
cuda=False,
# Whether the RL algorithm is on-policy or off-policy.
on_policy=False,
model=dict(
obs_shape=8,
action_shape=4,
encoder_hidden_size_list=[512, 64],
# Whether to use dueling head.
dueling=True,
),
# Reward's future discount factor, aka. gamma.
discount_factor=0.99,
# How many steps in td error.
nstep=nstep,
# learn_mode config
learn=dict(
update_per_collect=10,
batch_size=64,
learning_rate=0.001,
# Frequency of target network update.
target_update_freq=100,
),
# collect_mode config
collect=dict(
# You can use either "n_sample" or "n_episode" in collector.collect.
# Get "n_sample" samples per collect.
n_sample=64,
# Cut trajectories into pieces with length "unroll_len".
unroll_len=1,
),
# command_mode config
other=dict(
# Epsilon greedy with decay.
eps=dict(
# Decay type. Support ['exp', 'linear'].
type='exp',
start=0.95,
end=0.1,
decay=50000,
),
replay_buffer=dict(replay_buffer_size=100000, )
),
),
)
lunarlander_dqn_gail_config = EasyDict(lunarlander_dqn_gail_config)
main_config = lunarlander_dqn_gail_config
lunarlander_dqn_gail_create_config = dict(
env=dict(
type='lunarlander',
import_names=['dizoo.box2d.lunarlander.envs.lunarlander_env'],
),
env_manager=dict(type='subprocess'),
policy=dict(type='dqn'),
)
lunarlander_dqn_gail_create_config = EasyDict(lunarlander_dqn_gail_create_config)
create_config = lunarlander_dqn_gail_create_config
if __name__ == "__main__":
# or you can enter `ding -m serial_gail -c lunarlander_dqn_gail_config.py -s 0`
# then input the config you used to generate your expert model in the path mentioned above
# e.g. lunarlander_dqn_config.py
from ding.entry import serial_pipeline_gail
from dizoo.box2d.lunarlander.config import lunarlander_dqn_config, lunarlander_dqn_create_config
expert_main_config = lunarlander_dqn_config
expert_create_config = lunarlander_dqn_create_config
serial_pipeline_gail(
[main_config, create_config], [expert_main_config, expert_create_config],
max_env_step=1000000,
seed=0,
collect_data=True
)
|