mirror of
https://github.com/vale981/ray
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125 lines
3.5 KiB
Python
125 lines
3.5 KiB
Python
import os
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import pickle
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import numpy as np
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from ray.tune import result as tune_result
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from ray.rllib.agents.trainer import Trainer, with_common_config
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class _MockTrainer(Trainer):
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"""Mock trainer for use in tests"""
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_name = "MockTrainer"
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_default_config = with_common_config({
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"mock_error": False,
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"persistent_error": False,
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"test_variable": 1,
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"num_workers": 0,
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"user_checkpoint_freq": 0,
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"framework": "tf",
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})
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@classmethod
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def default_resource_request(cls, config):
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return None
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def _init(self, config, env_creator):
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self.info = None
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self.restored = False
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def step(self):
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if self.config["mock_error"] and self.iteration == 1 \
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and (self.config["persistent_error"] or not self.restored):
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raise Exception("mock error")
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result = dict(
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episode_reward_mean=10,
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episode_len_mean=10,
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timesteps_this_iter=10,
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info={})
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if self.config["user_checkpoint_freq"] > 0 and self.iteration > 0:
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if self.iteration % self.config["user_checkpoint_freq"] == 0:
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result.update({tune_result.SHOULD_CHECKPOINT: True})
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return result
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def save_checkpoint(self, checkpoint_dir):
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path = os.path.join(checkpoint_dir, "mock_agent.pkl")
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with open(path, "wb") as f:
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pickle.dump(self.info, f)
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return path
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def load_checkpoint(self, checkpoint_path):
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with open(checkpoint_path, "rb") as f:
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info = pickle.load(f)
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self.info = info
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self.restored = True
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def _register_if_needed(self, env_object):
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pass
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def set_info(self, info):
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self.info = info
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return info
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def get_info(self, sess=None):
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return self.info
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class _SigmoidFakeData(_MockTrainer):
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"""Trainer that returns sigmoid learning curves.
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This can be helpful for evaluating early stopping algorithms."""
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_name = "SigmoidFakeData"
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_default_config = with_common_config({
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"width": 100,
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"height": 100,
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"offset": 0,
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"iter_time": 10,
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"iter_timesteps": 1,
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"num_workers": 0,
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})
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def step(self):
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i = max(0, self.iteration - self.config["offset"])
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v = np.tanh(float(i) / self.config["width"])
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v *= self.config["height"]
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return dict(
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episode_reward_mean=v,
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episode_len_mean=v,
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timesteps_this_iter=self.config["iter_timesteps"],
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time_this_iter_s=self.config["iter_time"],
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info={})
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class _ParameterTuningTrainer(_MockTrainer):
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_name = "ParameterTuningTrainer"
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_default_config = with_common_config({
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"reward_amt": 10,
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"dummy_param": 10,
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"dummy_param2": 15,
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"iter_time": 10,
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"iter_timesteps": 1,
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"num_workers": 0,
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})
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def step(self):
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return dict(
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episode_reward_mean=self.config["reward_amt"] * self.iteration,
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episode_len_mean=self.config["reward_amt"],
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timesteps_this_iter=self.config["iter_timesteps"],
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time_this_iter_s=self.config["iter_time"],
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info={})
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def _trainer_import_failed(trace):
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"""Returns dummy agent class for if PyTorch etc. is not installed."""
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class _TrainerImportFailed(Trainer):
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_name = "TrainerImportFailed"
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_default_config = with_common_config({})
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def setup(self, config):
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raise ImportError(trace)
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return _TrainerImportFailed
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