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# Copyright (c) 2017-present, Facebook, Inc.
# All rights reserved.
#
# This source code is licensed under the BSD-style license found in the
# LICENSE file in the root directory of this source tree.
#!/usr/bin/env python
# -*- coding: utf-8 -*-
from datetime import datetime
import sys
import os
from rlpytorch import load_env, Evaluator, ArgsProvider, EvalIters
if __name__ == '__main__':
evaluator = Evaluator(stats=False)
eval_iters = EvalIters()
env, args = load_env(os.environ, overrides=dict(actor_only=True), eval_iters=eval_iters, evaluator=evaluator)
GC = env["game"].initialize_reduced_service()
model = env["model_loaders"][0].load_model(GC.params)
mi = env["mi"]
mi.add_model("actor", model, cuda=args.gpu is not None, gpu_id=args.gpu)
def reduced_project(batch):
output = mi["actor"].forward(batch.hist(0))
eval_iters.stats.feed_batch(batch)
return dict(reduced_s=output["h"].data)
def reduced_forward(batch):
b = batch.hist(0)
output = mi["actor"].transition(b["reduced_s"], b["a"])
return dict(reduced_next_s=output["hf"].data)
def reduced_predict(batch):
b = batch.hist(0)
output = mi["actor"].decision(b["reduced_s"])
return dict(pi=output["pi"].data, V=output["V"].data)
def actor(batch):
return evaluator.actor(batch)
evaluator.setup(mi=mi, sampler=env["sampler"])
if GC.reg_has_callback("actor"):
GC.reg_callback("actor", actor)
GC.reg_callback("reduced_predict", reduced_predict)
GC.reg_callback("reduced_forward", reduced_forward)
GC.reg_callback("reduced_project", reduced_project)
evaluator.episode_start(0)
GC.Start()
for n in eval_iters.iters():
GC.Run()
GC.Stop()
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