Source code for ray.rllib.optimizers.sync_batch_replay_optimizer

import random

import ray
from ray.rllib.evaluation.metrics import get_learner_stats
from ray.rllib.optimizers.policy_optimizer import PolicyOptimizer
from ray.rllib.policy.sample_batch import SampleBatch, DEFAULT_POLICY_ID, \
from ray.rllib.utils.annotations import override
from ray.rllib.utils.timer import TimerStat
from ray.rllib.utils.memory import ray_get_and_free

[docs]class SyncBatchReplayOptimizer(PolicyOptimizer): """Variant of the sync replay optimizer that replays entire batches. This enables RNN support. Does not currently support prioritization.""" def __init__(self, workers, learning_starts=1000, buffer_size=10000, train_batch_size=32): """Initialize a batch replay optimizer. Arguments: workers (WorkerSet): set of all workers learning_starts (int): start learning after this number of timesteps have been collected buffer_size (int): max timesteps to keep in the replay buffer train_batch_size (int): number of timesteps to train on at once """ PolicyOptimizer.__init__(self, workers) self.replay_starts = learning_starts self.max_buffer_size = buffer_size self.train_batch_size = train_batch_size assert self.max_buffer_size >= self.replay_starts # List of buffered sample batches self.replay_buffer = [] self.buffer_size = 0 # Stats self.update_weights_timer = TimerStat() self.sample_timer = TimerStat() self.grad_timer = TimerStat() self.learner_stats = {}
[docs] @override(PolicyOptimizer) def step(self): with self.update_weights_timer: if self.workers.remote_workers(): weights = ray.put(self.workers.local_worker().get_weights()) for e in self.workers.remote_workers(): e.set_weights.remote(weights) with self.sample_timer: if self.workers.remote_workers(): batches = ray_get_and_free( [e.sample.remote() for e in self.workers.remote_workers()]) else: batches = [self.workers.local_worker().sample()] # Handle everything as if multiagent tmp = [] for batch in batches: if isinstance(batch, SampleBatch): batch = MultiAgentBatch({ DEFAULT_POLICY_ID: batch }, batch.count) tmp.append(batch) batches = tmp for batch in batches: if batch.count > self.max_buffer_size: raise ValueError( "The size of a single sample batch exceeds the replay " "buffer size ({} > {})".format(batch.count, self.max_buffer_size)) self.replay_buffer.append(batch) self.num_steps_sampled += batch.count self.buffer_size += batch.count while self.buffer_size > self.max_buffer_size: evicted = self.replay_buffer.pop(0) self.buffer_size -= evicted.count if self.num_steps_sampled >= self.replay_starts: return self._optimize() else: return {}
[docs] @override(PolicyOptimizer) def stats(self): return dict( PolicyOptimizer.stats(self), **{ "sample_time_ms": round(1000 * self.sample_timer.mean, 3), "grad_time_ms": round(1000 * self.grad_timer.mean, 3), "update_time_ms": round(1000 * self.update_weights_timer.mean, 3), "opt_peak_throughput": round(self.grad_timer.mean_throughput, 3), "opt_samples": round(self.grad_timer.mean_units_processed, 3), "learner": self.learner_stats, })
def _optimize(self): samples = [random.choice(self.replay_buffer)] while sum(s.count for s in samples) < self.train_batch_size: samples.append(random.choice(self.replay_buffer)) samples = SampleBatch.concat_samples(samples) with self.grad_timer: info_dict = self.workers.local_worker().learn_on_batch(samples) for policy_id, info in info_dict.items(): self.learner_stats[policy_id] = get_learner_stats(info) self.grad_timer.push_units_processed(samples.count) self.num_steps_trained += samples.count return info_dict