[rllib] Improve performance for small rollouts

python/ray/rllib/policy_gradient/agent.py

@@ -8,6 +8,7 @@
 
 from tensorflow.python import debug as tf_debug
 
+import numpy as np
 import ray
 
 from ray.rllib.parallel import LocalSyncParallelOptimizer
@@ -16,6 +17,7 @@
 from ray.rllib.policy_gradient.loss import ProximalPolicyLoss
 from ray.rllib.policy_gradient.filter import MeanStdFilter
 from ray.rllib.policy_gradient.rollout import rollouts, add_advantage_values
+from ray.rllib.policy_gradient.utils import flatten, concatenate
 
 # TODO(pcm): Make sure that both observation_filter and reward_filter
 # are correctly handled, i.e. (a) the values are accumulated accross
@@ -82,8 +84,8 @@ def __init__(
         assert config["sgd_batchsize"] % len(devices) == 0, \
             "Batch size must be evenly divisible by devices"
         if is_remote:
-            self.batch_size = 1
-            self.per_device_batch_size = 1
+            self.batch_size = config["rollout_batchsize"]
+            self.per_device_batch_size = config["rollout_batchsize"]
         else:
             self.batch_size = config["sgd_batchsize"]
             self.per_device_batch_size = int(self.batch_size / len(devices))
@@ -148,11 +150,51 @@ def load_weights(self, weights):
         self.variables.set_weights(weights)
 
     def compute_trajectory(self, gamma, lam, horizon):
+        """Compute a single rollout on the agent and return."""
         trajectory = rollouts(
             self.common_policy,
             self.env, horizon, self.observation_filter, self.reward_filter)
         add_advantage_values(trajectory, gamma, lam, self.reward_filter)
         return trajectory
 
+    def compute_steps(self, gamma, lam, horizon, min_steps_per_task=-1):
+        """Compute multiple rollouts and concatenate the results.
+
+        Args:
+            gamma: MDP discount factor
+            lam: GAE(lambda) parameter
+            horizon: Number of steps after which a rollout gets cut
+            min_steps_per_task: Lower bound on the number of states to be
+                collected.
+
+        Returns:
+            states: List of states.
+            total_rewards: Total rewards of the trajectories.
+            trajectory_lengths: Lengths of the trajectories.
+        """
+        num_steps_so_far = 0
+        trajectories = []
+        total_rewards = []
+        trajectory_lengths = []
+        while True:
+            trajectory = self.compute_trajectory(gamma, lam, horizon)
+            total_rewards.append(
+                trajectory["raw_rewards"].sum(axis=0).mean())
+            trajectory_lengths.append(
+                np.logical_not(trajectory["dones"]).sum(axis=0).mean())
+            trajectory = flatten(trajectory)
+            not_done = np.logical_not(trajectory["dones"])
+            # Filtering out states that are done. We do this because
+            # trajectories are batched and cut only if all the trajectories
+            # in the batch terminated, so we can potentially get rid of
+            # some of the states here.
+            trajectory = {key: val[not_done]
+                          for key, val in trajectory.items()}
+            num_steps_so_far += trajectory["raw_rewards"].shape[0]
+            trajectories.append(trajectory)
+            if num_steps_so_far >= min_steps_per_task:
+                break
+        return concatenate(trajectories), total_rewards, trajectory_lengths
+
 
 RemoteAgent = ray.remote(Agent)

python/ray/rllib/policy_gradient/policy_gradient.py

@@ -18,10 +18,19 @@
 
 
 DEFAULT_CONFIG = {
+    # Discount factor of the MDP
     "gamma": 0.995,
+    # Number of steps after which the rollout gets cut
+    "horizon": 2000,
+    # GAE(lambda) parameter
+    "lambda": 1.0,
+    # Initial coefficient for KL divergence
     "kl_coeff": 0.2,
+    # Number of SGD iterations in each outer loop
     "num_sgd_iter": 30,
+    # Number of outer loop iterations
     "max_iterations": 1000,
+    # Stepsize of SGD
     "sgd_stepsize": 5e-5,
     # TODO(pcm): Expose the choice between gpus and cpus
     # as a command line argument.
@@ -31,17 +40,34 @@
         "log_device_placement": False,
         "allow_soft_placement": True,
     },
-    "sgd_batchsize": 128,  # total size across all devices
+    # Batch size for policy evaluations for rollouts
+    "rollout_batchsize": 1,
+    # Total SGD batch size across all devices for SGD
+    "sgd_batchsize": 128,
+    # Coefficient of the entropy regularizer
     "entropy_coeff": 0.0,
+    # PPO clip parameter
     "clip_param": 0.3,
+    # Target value for KL divergence
     "kl_target": 0.01,
     "model": {"free_logstd": False},
+    # Number of timesteps collected in each outer loop
     "timesteps_per_batch": 40000,
+    # Each tasks performs rollouts until at least this
+    # number of steps is obtained
+    "min_steps_per_task": 1000,
+    # Number of actors used to collect the rollouts
     "num_agents": 5,
+    # Dump TensorFlow timeline after this many SGD minibatches
     "full_trace_nth_sgd_batch": -1,
+    # Whether to profile data loading
     "full_trace_data_load": False,
+    # If this is True, the TensorFlow debugger is invoked if an Inf or NaN
+    # is detected
     "use_tf_debugger": False,
-    "write_logs": True,  # write checkpoints and tensorflow logging?
+    # If True, we write checkpoints and tensorflow logging
+    "write_logs": True,
+    # Name of the model checkpoint file
     "model_checkpoint_file": "iteration-%s.ckpt"}
 
 
@@ -79,6 +105,7 @@ def __init__(self, env_name, config, upload_dir=None):
                 self.env_name, 1, self.preprocessor, self.config,
                 self.logdir, True)
             for _ in range(config["num_agents"])]
+        self.start_time = time.time()
 
     def train(self):
         agents = self.agents
@@ -108,7 +135,7 @@ def train(self):
         weights = ray.put(model.get_weights())
         [a.load_weights.remote(weights) for a in agents]
         trajectory, total_reward, traj_len_mean = collect_samples(
-            agents, config["timesteps_per_batch"], config["gamma"], 1.0, 2000)
+            agents, config)
         print("total reward is ", total_reward)
         print("trajectory length mean is ", traj_len_mean)
         print("timesteps:", trajectory["dones"].shape[0])
@@ -213,6 +240,7 @@ def train(self):
         print("load time:", load_time)
         print("sgd time:", sgd_time)
         print("sgd examples/s:", len(trajectory["observations"]) / sgd_time)
+        print("total time so far:", time.time() - self.start_time)
 
         result = TrainingResult(
             self.experiment_id.hex, j, total_reward, traj_len_mean, info)

python/ray/rllib/policy_gradient/rollout.py

@@ -6,7 +6,7 @@
 import ray
 
 from ray.rllib.policy_gradient.filter import NoFilter
-from ray.rllib.policy_gradient.utils import flatten, concatenate
+from ray.rllib.policy_gradient.utils import concatenate
 
 
 def rollouts(policy, env, horizon, observation_filter=NoFilter(),
@@ -72,42 +72,36 @@ def add_advantage_values(trajectory, gamma, lam, reward_filter):
     trajectory["advantages"] = advantages
 
 
-@ray.remote
-def compute_trajectory(policy, env, gamma, lam, horizon, observation_filter,
-                       reward_filter):
-    trajectory = rollouts(policy, env, horizon, observation_filter,
-                          reward_filter)
-    add_advantage_values(trajectory, gamma, lam, reward_filter)
-    return trajectory
-
-
-def collect_samples(agents, num_timesteps, gamma, lam, horizon,
-                    observation_filter=NoFilter(), reward_filter=NoFilter()):
+def collect_samples(agents,
+                    config,
+                    observation_filter=NoFilter(),
+                    reward_filter=NoFilter()):
     num_timesteps_so_far = 0
     trajectories = []
     total_rewards = []
-    traj_len_means = []
+    trajectory_lengths = []
     # This variable maps the object IDs of trajectories that are currently
     # computed to the agent that they are computed on; we start some initial
     # tasks here.
-    agent_dict = {agent.compute_trajectory.remote(gamma, lam, horizon):
+    agent_dict = {agent.compute_steps.remote(
+                      config["gamma"], config["lambda"],
+                      config["horizon"], config["min_steps_per_task"]):
                   agent for agent in agents}
-    while num_timesteps_so_far < num_timesteps:
+    while num_timesteps_so_far < config["timesteps_per_batch"]:
         # TODO(pcm): Make wait support arbitrary iterators and remove the
         # conversion to list here.
         [next_trajectory], waiting_trajectories = ray.wait(
             list(agent_dict.keys()))
         agent = agent_dict.pop(next_trajectory)
         # Start task with next trajectory and record it in the dictionary.
-        agent_dict[agent.compute_trajectory.remote(gamma, lam, horizon)] = (
+        agent_dict[agent.compute_steps.remote(
+                       config["gamma"], config["lambda"],
+                       config["horizon"], config["min_steps_per_task"])] = (
             agent)
-        trajectory = flatten(ray.get(next_trajectory))
-        not_done = np.logical_not(trajectory["dones"])
-        total_rewards.append(
-            trajectory["raw_rewards"][not_done].sum(axis=0).mean())
-        traj_len_means.append(not_done.sum(axis=0).mean())
-        trajectory = {key: val[not_done] for key, val in trajectory.items()}
+        trajectory, rewards, lengths = ray.get(next_trajectory)
+        total_rewards.extend(rewards)
+        trajectory_lengths.extend(lengths)
         num_timesteps_so_far += len(trajectory["dones"])
         trajectories.append(trajectory)
     return (concatenate(trajectories), np.mean(total_rewards),
-            np.mean(traj_len_means))
+            np.mean(trajectory_lengths))