mirror of
https://github.com/wassname/ray.git
synced 2026-06-28 00:44:50 +08:00
Roy Fox
4b0ef5eb2c
* Behavior Cloning * episode_reward_mean -> mean_loss * removing vestigial code * punctuation * unnecessary * Behavior Cloning * Behavior Cloning * Update __init__.py
46 lines
1.5 KiB
Python
46 lines
1.5 KiB
Python
from __future__ import absolute_import
|
|
from __future__ import division
|
|
from __future__ import print_function
|
|
|
|
|
|
class Optimizer(object):
|
|
"""RLlib optimizers encapsulate distributed RL optimization strategies.
|
|
|
|
For example, AsyncOptimizer is used for A3C, and LocalMultiGPUOptimizer is
|
|
used for PPO. These optimizers are all pluggable, and it is possible
|
|
to mix and match as needed.
|
|
|
|
In order for an algorithm to use an RLlib optimizer, it must implement
|
|
the Evaluator interface and pass a number of Evaluators to its Optimizer
|
|
of choice. The Optimizer uses these Evaluators to sample from the
|
|
environment and compute model gradient updates.
|
|
"""
|
|
|
|
def __init__(self, config, local_evaluator, remote_evaluators):
|
|
"""Create an optimizer instance.
|
|
|
|
Args:
|
|
config (dict): Optimizer-specific configuration data.
|
|
local_evaluator (Evaluator): Local evaluator instance, required.
|
|
remote_evaluators (list): A list of handles to remote evaluators.
|
|
if empty, the optimizer should fall back to to using only the
|
|
local evaluator.
|
|
"""
|
|
self.config = config
|
|
self.local_evaluator = local_evaluator
|
|
self.remote_evaluators = remote_evaluators
|
|
self._init()
|
|
|
|
def _init(self):
|
|
pass
|
|
|
|
def step(self):
|
|
"""Takes a logical optimization step."""
|
|
|
|
raise NotImplementedError
|
|
|
|
def stats(self):
|
|
"""Returns a dictionary of internal performance statistics."""
|
|
|
|
return {}
|