Added asynchronous plotting to TensorFlow

.gitignore

@@ -36,4 +36,3 @@ blackbox.zip
 blackbox
 rllab/config_personal.py
 *.swp
-sandbox

sandbox/rocky/tf/algos/batch_polopt.py

@@ -1,11 +1,14 @@
 import time
+
+import tensorflow as tf
+
 from rllab.algos import RLAlgorithm
 import rllab.misc.logger as logger
+
+from sandbox.rocky.tf.plotter import Plotter
 from sandbox.rocky.tf.policies.base import Policy
-import tensorflow as tf
 from sandbox.rocky.tf.samplers import BatchSampler
 from sandbox.rocky.tf.samplers import VectorizedSampler
-from rllab.sampler.utils import rollout
 
 
 class BatchPolopt(RLAlgorithm):
@@ -14,30 +17,28 @@ class BatchPolopt(RLAlgorithm):
     This includes various policy gradient methods like vpg, npg, ppo, trpo, etc.
     """
 
-    def __init__(
-            self,
-            env,
-            policy,
-            baseline,
-            scope=None,
-            n_itr=500,
-            start_itr=0,
-            batch_size=5000,
-            max_path_length=500,
-            discount=0.99,
-            gae_lambda=1,
-            plot=False,
-            pause_for_plot=False,
-            center_adv=True,
-            positive_adv=False,
-            store_paths=False,
-            whole_paths=True,
-            fixed_horizon=False,
-            sampler_cls=None,
-            sampler_args=None,
-            force_batch_sampler=False,
-            **kwargs
-    ):
+    def __init__(self,
+                 env,
+                 policy,
+                 baseline,
+                 scope=None,
+                 n_itr=500,
+                 start_itr=0,
+                 batch_size=5000,
+                 max_path_length=500,
+                 discount=0.99,
+                 gae_lambda=1,
+                 plot=False,
+                 pause_for_plot=False,
+                 center_adv=True,
+                 positive_adv=False,
+                 store_paths=False,
+                 whole_paths=True,
+                 fixed_horizon=False,
+                 sampler_cls=None,
+                 sampler_args=None,
+                 force_batch_sampler=False,
+                 **kwargs):
         """
         :param env: Environment
         :param policy: Policy
@@ -86,11 +87,16 @@ def __init__(
         self.sampler = sampler_cls(self, **sampler_args)
         self.init_opt()
 
-    def start_worker(self):
+    def start_worker(self, sess):
         self.sampler.start_worker()
+        if self.plot:
+            self.plotter = Plotter(self.env, self.policy, sess)
+            self.plotter.start()
 
     def shutdown_worker(self):
         self.sampler.shutdown_worker()
+        if self.plot:
+            self.plotter.shutdown()
 
     def obtain_samples(self, itr):
         return self.sampler.obtain_samples(itr)
@@ -103,9 +109,9 @@ def train(self, sess=None):
         if sess is None:
             sess = tf.Session()
             sess.__enter__()
-            
+
         sess.run(tf.global_variables_initializer())
-        self.start_worker()
+        self.start_worker(sess)
         start_time = time.time()
         for itr in range(self.start_itr, self.n_itr):
             itr_start_time = time.time()
@@ -119,7 +125,7 @@ def train(self, sess=None):
                 logger.log("Optimizing policy...")
                 self.optimize_policy(itr, samples_data)
                 logger.log("Saving snapshot...")
-                params = self.get_itr_snapshot(itr, samples_data)  # , **kwargs)
+                params = self.get_itr_snapshot(itr, samples_data)
                 if self.store_paths:
                     params["paths"] = samples_data["paths"]
                 logger.save_itr_params(itr, params)
@@ -128,10 +134,11 @@ def train(self, sess=None):
                 logger.record_tabular('ItrTime', time.time() - itr_start_time)
                 logger.dump_tabular(with_prefix=False)
                 if self.plot:
-                    rollout(self.env, self.policy, animated=True, max_path_length=self.max_path_length)
+                    self.update_plot()
                     if self.pause_for_plot:
                         input("Plotting evaluation run: Press Enter to "
                               "continue...")
+
         self.shutdown_worker()
         if created_session:
             sess.close()
@@ -158,3 +165,6 @@ def get_itr_snapshot(self, itr, samples_data):
     def optimize_policy(self, itr, samples_data):
         raise NotImplementedError
 
+    def update_plot(self):
+        if self.plot:
+            self.plotter.update_plot(self.policy, self.max_path_length)

sandbox/rocky/tf/plotter/__init__.py

@@ -0,0 +1 @@
+from sandbox.rocky.tf.plotter.plotter import Plotter

sandbox/rocky/tf/plotter/plotter.py

@@ -0,0 +1,89 @@
+import atexit
+from multiprocessing import Process
+import numpy as np
+import platform
+from queue import Empty, Queue
+import tensorflow as tf
+from threading import Thread
+
+from rllab.sampler.utils import rollout
+
+__all__ = ["Plotter"]
+
+
+class Plotter(object):
+    def __init__(self, env, policy, sess=None, graph=None):
+        self.env = env
+        self.policy = policy
+        self.sess = tf.get_default_session() if sess is None else sess
+        self.graph = tf.get_default_graph() if graph is None else graph
+        self.worker_thread = Thread(target=self._start_worker, daemon=True)
+        self.queue = Queue()
+
+    def _start_worker(self):
+        env = None
+        policy = None
+        max_length = None
+        initial_rollout = True
+        try:
+            with self.sess.as_default(), self.sess.graph.as_default():
+                while True:
+                    msgs = {}
+                    # If true, block and yield processor
+                    if initial_rollout:
+                        msg = self.queue.get()
+                        msgs[msg[0]] = msg[1:]
+                        # Only fetch the last message of each type
+                        while not self.queue.empty():
+                            msg = self.queue.get()
+                            msgs[msg[0]] = msg[1:]
+                    else:
+                        # Only fetch the last message of each type
+                        while not self.queue.empty():
+                            msg = self.queue.get_nowait()
+                            msgs[msg[0]] = msg[1:]
+
+                    if 'stop' in msgs:
+                        break
+                    elif 'update' in msgs:
+                        env, policy = msgs['update']
+                    elif 'demo' in msgs:
+                        param_values, max_length = msgs['demo']
+                        policy.set_param_values(param_values)
+                        initial_rollout = False
+                        rollout(
+                            env,
+                            policy,
+                            max_path_length=max_length,
+                            animated=True,
+                            speedup=5)
+                    else:
+                        if max_length:
+                            rollout(
+                                env,
+                                policy,
+                                max_path_length=max_length,
+                                animated=True,
+                                speedup=5)
+        except KeyboardInterrupt:
+            pass
+
+    def shutdown(self):
+        if self.worker_thread.is_alive():
+            self.queue.put(['stop'])
+            self.queue.task_done()
+            self.queue.join()
+            self.worker_thread.join()
+
+    def start(self):
+        if not self.worker_thread.is_alive():
+            tf.get_variable_scope().reuse_variables()
+            self.worker_thread.start()
+            self.queue.put(['update', self.env, self.policy])
+            self.queue.task_done()
+            atexit.register(self.shutdown)
+
+    def update_plot(self, policy, max_length=np.inf):
+        if self.worker_thread.is_alive():
+            self.queue.put(['demo', policy.get_param_values(), max_length])
+            self.queue.task_done()