[tune] Added Population Based Training (#1355)

Adds a Population-Based Training (as described in https://arxiv.org/abs/1711.09846) scheduler to Ray.tune. Currently mutates hyperparameters according to either a user-defined list of possible values to mutate to (necessary if hyperparameters can only be certain values ex. sgd_batch_size), or by a factor of 0.8 or 1.2.

python/ray/rllib/__init__.py

@@ -8,8 +8,8 @@ from ray.tune.registry import register_trainable
 
 
 def _register_all():
-    for key in [
-            "PPO", "ES", "DQN", "A3C", "BC", "__fake", "__sigmoid_fake_data"]:
+    for key in ["PPO", "ES", "DQN", "A3C", "BC", "__fake",
+                "__sigmoid_fake_data", "__parameter_tuning"]:
         try:
             from ray.rllib.agent import get_agent_class
             register_trainable(key, get_agent_class(key))

python/ray/rllib/agent.py

@@ -190,6 +190,25 @@ class _SigmoidFakeData(_MockAgent):
             time_this_iter_s=self.config["iter_time"], info={})
 
 
+class _ParameterTuningAgent(_MockAgent):
+
+    _agent_name = "ParameterTuningAgent"
+    _default_config = {
+        "reward_amt": 10,
+        "dummy_param": 10,
+        "dummy_param2": 15,
+        "iter_time": 10,
+        "iter_timesteps": 1
+    }
+
+    def _train(self):
+        return TrainingResult(
+            episode_reward_mean=self.config["reward_amt"] * self.iteration,
+            episode_len_mean=self.config["reward_amt"],
+            timesteps_this_iter=self.config["iter_timesteps"],
+            time_this_iter_s=self.config["iter_time"], info={})
+
+
 def get_agent_class(alg):
     """Returns the class of an known agent given its name."""
 
@@ -215,6 +234,8 @@ def get_agent_class(alg):
         return _MockAgent
     elif alg == "__sigmoid_fake_data":
         return _SigmoidFakeData
+    elif alg == "__parameter_tuning":
+        return _ParameterTuningAgent
     else:
         raise Exception(
             ("Unknown algorithm {}.").format(alg))

python/ray/tune/pbt.py

@@ -0,0 +1,190 @@
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import collections
+import random
+import math
+import copy
+
+from ray.tune.trial_scheduler import FIFOScheduler, TrialScheduler
+
+
+class PopulationBasedTraining(FIFOScheduler):
+    """Implements the Population Based Training algorithm as described in the
+    PBT paper (https://arxiv.org/abs/1711.09846)(Experimental):
+
+    Args:
+        time_attr (str): The TrainingResult attr to use for documenting length
+            of time since last ready() call. Attribute only has to increase
+            monotonically.
+        reward_attr (str): The TrainingResult objective value attribute. As
+            with 'time_attr'. this may refer to any objective value that
+            is supposed to increase with time.
+        grace_period (float): Period of time, in which algorithm will not
+            compare model to other models.
+        perturbation_interval (float): Used in the truncation ready function to
+            determine if enough time has passed so that a agent can be tested
+            for readiness.
+        hyperparameter_mutations (dict); Possible values that each
+            hyperparameter can mutate to, as certain hyperparameters
+            only work with certain values.
+    """
+
+    def __init__(
+            self, time_attr='training_iteration',
+            reward_attr='episode_reward_mean',
+            grace_period=10.0, perturbation_interval=6.0,
+            hyperparameter_mutations=None):
+        FIFOScheduler.__init__(self)
+        self._completed_trials = set()
+        self._results = collections.defaultdict(list)
+        self._last_perturbation_time = {}
+        self._grace_period = grace_period
+        self._reward_attr = reward_attr
+        self._time_attr = time_attr
+
+        self._hyperparameter_mutations = hyperparameter_mutations
+        self._perturbation_interval = perturbation_interval
+        self._checkpoint_paths = {}
+
+    def on_trial_result(self, trial_runner, trial, result):
+
+        self._results[trial].append(result)
+        time = getattr(result, self._time_attr)
+        # check model is ready to undergo mutation, based on user
+        # function or default function
+        self._checkpoint_paths[trial] = trial.checkpoint()
+        if time > self._grace_period:
+            ready = self._truncation_ready(result, trial, time)
+        else:
+            ready = False
+        if ready:
+            print("ready to undergo mutation")
+            print("----")
+            print("Current Trial is: {0}".format(trial))
+            # get best trial for current time
+            best_trial = self._get_best_trial(result, time)
+            print("Best Trial is: {0}".format(best_trial))
+            print(best_trial.config)
+
+            # if current trial is the best trial (as in same hyperparameters),
+            # do nothing
+            if trial.config == best_trial.config:
+                print("current trial is best trial")
+                return TrialScheduler.CONTINUE
+            else:
+                self._exploit(self._hyperparameter_mutations, best_trial,
+                              trial, trial_runner, time)
+                return TrialScheduler.CONTINUE
+        return TrialScheduler.CONTINUE
+
+    def on_trial_complete(self, trial_runner, trial, result):
+        self._results[trial].append(result)
+        self._completed_trials.add(trial)
+
+    def _exploit(self, hyperparameter_mutations, best_trial,
+                 trial, trial_runner, time):
+        trial.stop()
+        mutate_string = "_mutated@" + str(time)
+        hyperparams = copy.deepcopy(best_trial.config)
+        hyperparams = self._explore(hyperparams, hyperparameter_mutations,
+                                    best_trial)
+        print("new hyperparameter configuration: {0}".format(hyperparams))
+        checkpoint = self._checkpoint_paths[best_trial]
+        trial._checkpoint_path = checkpoint
+        trial.config = hyperparams
+        trial.experiment_tag = trial.experiment_tag + mutate_string
+        trial.start()
+
+    def _explore(self, hyperparams, hyperparameter_mutations, best_trial):
+        if hyperparameter_mutations is not None:
+            hyperparams = {
+                param: random.choice(hyperparameter_mutations[param])
+                for param in hyperparams
+                if param != "env" and param in hyperparameter_mutations
+            }
+            for param in best_trial.config:
+                if param not in hyperparameter_mutations and param != "env":
+                    hyperparams[param] = math.ceil(
+                        (best_trial.config[param]
+                         * random.choice([0.8, 1.2])/2.)) * 2
+        else:
+            hyperparams = {
+                param: math.ceil(
+                    (random.choice([0.8, 1.2]) *
+                     hyperparams[param])/2.) * 2
+                for param in hyperparams
+                if param != "env"
+            }
+        hyperparams["env"] = best_trial.config["env"]
+        return hyperparams
+
+    def _truncation_ready(self, result, trial, time):
+        # function checks if appropriate time has passed
+        # and trial is in the bottom 20% of all trials, and if so, is ready
+        if trial not in self._last_perturbation_time:
+            print("added trial to time tracker")
+            self._last_perturbation_time[trial] = (time)
+        else:
+            time_since_last = time - self._last_perturbation_time[trial]
+            if time_since_last >= self._perturbation_interval:
+                self._last_perturbation_time[trial] = time
+                sorted_result_keys = sorted(
+                    self._results, key=lambda x:
+                    max(self._results.get(x) if self._results.get(x) else [0])
+                )
+                max_index = int(round(len(sorted_result_keys) * 0.2))
+                for i in range(0, max_index):
+                    if trial == sorted_result_keys[i]:
+                        print("{0} is in the bottomn 20 percent of {1}, \
+                        truncation is ready".format(
+                            trial,
+                            [x.experiment_tag for x in sorted_result_keys]
+                        ))
+                        return True
+                print("{0} is not in the bottomn 20 percent of {1}, \
+                truncation is not ready".format(
+                    trial,
+                    [x.experiment_tag for x in sorted_result_keys]
+                ))
+            else:
+                print("not enough time has passed since last mutation")
+        return False
+
+    def _get_best_trial(self, result, time):
+        results_at_time = {}
+        for trial in self._results:
+            results_at_time[trial] = [
+                getattr(r, self._reward_attr)
+                for r in self._results[trial]
+                if getattr(r, self._time_attr) <= time
+            ]
+        print("Results at {0}: {1}".format(time, results_at_time))
+        return max(results_at_time, key=lambda x:
+                   max(results_at_time.get(x)
+                       if results_at_time.get(x) else [0]))
+
+    def _is_empty(self, x):
+        if x:
+            return False
+        return True
+
+    def debug_string(self):
+
+        min_time = 0
+        best_trial = None
+        for trial in self._completed_trials:
+            last_result = self._results[trial][-1]
+            if (getattr(last_result, self._time_attr)
+                    < min_time or min_time == 0):
+                min_time = getattr(last_result, self._time_attr)
+                best_trial = trial
+        if best_trial is not None:
+            return ("The Best Trial is currently {0} finishing in {1} iterations, \
+                    with the hyperparameters of {2}".format(
+                                    best_trial, min_time, best_trial.config
+                                )
+                    )
+        else:
+            return "PBT has started"

python/ray/tune/trial_runner.py

@@ -188,8 +188,8 @@ class TrialRunner(object):
         trial = self._get_runnable()
         return trial is not None
 
-    def _launch_trial(self):
-        trial = self._get_runnable()
+    def _launch_trial(self, custom_trial=None):
+        trial = custom_trial or self._get_runnable()
         self._commit_resources(trial.resources)
         try:
             trial.start()