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@mawright @ericl
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from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
"""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,
},
},
},
)
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