def _write_dict_summaries(dictionary, writer, name, env_steps):
for d, key, value in iterate_recursively(dictionary):
if isinstance(value, bool):
value = int(value)
if isinstance(value, (int, float)):
writer.add_scalar(f'zz_pbt/{name}_{key}', value, env_steps)
elif isinstance(value, (tuple, list)):
for i, tuple_value in enumerate(value):
writer.add_scalar(f'zz_pbt/{name}_{key}_{i}', tuple_value, env_steps)
else:
log.error('Unsupported type in pbt summaries %r', type(value))
def process_report(self, report):
"""Process stats from various types of workers."""
if 'policy_id' in report:
policy_id = report['policy_id']
if 'learner_env_steps' in report:
if policy_id in self.env_steps:
delta = report['learner_env_steps'] - self.env_steps[
policy_id]
self.total_env_steps_since_resume += delta
self.env_steps[policy_id] = report['learner_env_steps']
if 'episodic' in report:
s = report['episodic']
for _, key, value in iterate_recursively(s):
if key not in self.policy_avg_stats:
self.policy_avg_stats[key] = [
deque(maxlen=self.cfg.stats_avg)
for _ in range(self.cfg.num_policies)
]
self.policy_avg_stats[key][policy_id].append(value)
for extra_stat_func in EXTRA_EPISODIC_STATS_PROCESSING:
extra_stat_func(policy_id, key, value, self.cfg)
if 'train' in report:
self.report_train_summaries(report['train'], policy_id)
if 'samples' in report:
self.samples_collected[policy_id] += report['samples']
if 'timing' in report:
for k, v in report['timing'].items():
if k not in self.avg_stats:
self.avg_stats[k] = deque([], maxlen=50)
self.avg_stats[k].append(v)
if 'stats' in report:
self.stats.update(report['stats'])
def ensure_memory_shared(*tensors):
"""To prevent programming errors, ensure all tensors are in shared memory."""
for tensor_dict in tensors:
for _, _, t in iterate_recursively(tensor_dict):
assert t.is_shared()