mirror of
https://github.com/vale981/ray
synced 2025-03-06 10:31:39 -05:00
60d4d5e1aa
* Remove all __future__ imports from RLlib. * Remove (object) again from tf_run_builder.py::TFRunBuilder. * Fix 2xLINT warnings. * Fix broken appo_policy import (must be appo_tf_policy) * Remove future imports from all other ray files (not just RLlib). * Remove future imports from all other ray files (not just RLlib). * Remove future import blocks that contain `unicode_literals` as well. Revert appo_tf_policy.py to appo_policy.py (belongs to another PR). * Add two empty lines before Schedule class. * Put back __future__ imports into determine_tests_to_run.py. Fails otherwise on a py2/print related error.
100 lines
3.4 KiB
Python
100 lines
3.4 KiB
Python
"""Example of using custom_loss() with an imitation learning loss.
|
|
|
|
The default input file is too small to learn a good policy, but you can
|
|
generate new experiences for IL training as follows:
|
|
|
|
To generate experiences:
|
|
$ ./train.py --run=PG --config='{"output": "/tmp/cartpole"}' --env=CartPole-v0
|
|
|
|
To train on experiences with joint PG + IL loss:
|
|
$ python custom_loss.py --input-files=/tmp/cartpole
|
|
"""
|
|
|
|
import argparse
|
|
import os
|
|
|
|
import ray
|
|
from ray import tune
|
|
from ray.rllib.models import Model, ModelCatalog
|
|
from ray.rllib.models.tf.tf_action_dist import Categorical
|
|
from ray.rllib.models.tf.fcnet_v1 import FullyConnectedNetwork
|
|
from ray.rllib.models.model import restore_original_dimensions
|
|
from ray.rllib.offline import JsonReader
|
|
from ray.rllib.utils import try_import_tf
|
|
|
|
tf = try_import_tf()
|
|
|
|
parser = argparse.ArgumentParser()
|
|
parser.add_argument("--iters", type=int, default=200)
|
|
parser.add_argument(
|
|
"--input-files",
|
|
type=str,
|
|
default=os.path.join(
|
|
os.path.dirname(os.path.abspath(__file__)),
|
|
"../tests/data/cartpole_small"))
|
|
|
|
|
|
class CustomLossModel(Model):
|
|
"""Custom model that adds an imitation loss on top of the policy loss."""
|
|
|
|
def _build_layers_v2(self, input_dict, num_outputs, options):
|
|
self.obs_in = input_dict["obs"]
|
|
with tf.variable_scope("shared", reuse=tf.AUTO_REUSE):
|
|
self.fcnet = FullyConnectedNetwork(input_dict, self.obs_space,
|
|
self.action_space, num_outputs,
|
|
options)
|
|
return self.fcnet.outputs, self.fcnet.last_layer
|
|
|
|
def custom_loss(self, policy_loss, loss_inputs):
|
|
# create a new input reader per worker
|
|
reader = JsonReader(self.options["custom_options"]["input_files"])
|
|
input_ops = reader.tf_input_ops()
|
|
|
|
# define a secondary loss by building a graph copy with weight sharing
|
|
obs = tf.cast(input_ops["obs"], tf.float32)
|
|
logits, _ = self._build_layers_v2({
|
|
"obs": restore_original_dimensions(obs, self.obs_space)
|
|
}, self.num_outputs, self.options)
|
|
|
|
# You can also add self-supervised losses easily by referencing tensors
|
|
# created during _build_layers_v2(). For example, an autoencoder-style
|
|
# loss can be added as follows:
|
|
# ae_loss = squared_diff(
|
|
# loss_inputs["obs"], Decoder(self.fcnet.last_layer))
|
|
print("FYI: You can also use these tensors: {}, ".format(loss_inputs))
|
|
|
|
# compute the IL loss
|
|
action_dist = Categorical(logits, self.options)
|
|
self.policy_loss = policy_loss
|
|
self.imitation_loss = tf.reduce_mean(
|
|
-action_dist.logp(input_ops["actions"]))
|
|
return policy_loss + 10 * self.imitation_loss
|
|
|
|
def custom_stats(self):
|
|
return {
|
|
"policy_loss": self.policy_loss,
|
|
"imitation_loss": self.imitation_loss,
|
|
}
|
|
|
|
|
|
if __name__ == "__main__":
|
|
ray.init()
|
|
args = parser.parse_args()
|
|
|
|
ModelCatalog.register_custom_model("custom_loss", CustomLossModel)
|
|
tune.run(
|
|
"PG",
|
|
stop={
|
|
"training_iteration": args.iters,
|
|
},
|
|
config={
|
|
"env": "CartPole-v0",
|
|
"num_workers": 0,
|
|
"model": {
|
|
"custom_model": "custom_loss",
|
|
"custom_options": {
|
|
"input_files": args.input_files,
|
|
},
|
|
},
|
|
},
|
|
)
|