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* reordering of the episode reset operation and allowing to store episodes only when they are terminated * reordering of the episode reset operation and allowing to store episodes only when they are terminated * revert tensorflow-gpu to 1.9.0 + bug fix in should_train() * tests readme file and refactoring of policy optimization agent train function * Update README.md * Update README.md * additional policy optimization train function simplifications * Updated the traces after the reordering of the environment reset * docker and jenkins files * updated the traces to the ones from within the docker container * updated traces and added control suite to the docker * updated jenkins file with the intel proxy + updated doom basic a3c test params * updated line breaks in jenkins file * added a missing line break in jenkins file * refining trace tests ignored presets + adding a configurable beta entropy value * switch the order of trace and golden tests in jenkins + fix golden tests processes not killed issue * updated benchmarks for dueling ddqn breakout and pong * allowing dynamic updates to the loss weights + bug fix in episode.update_returns * remove docker and jenkins file
Block Factory
The block factory is a class which creates a block that fits into a specific RL scheme. Example RL schemes are: self play, multi agent, HRL, basic RL, etc. The block factory should create all the components of the block and return the block scheduler. The block factory will then be used to create different combinations of components. For example, an HRL factory can be later instantiated with:
- env = Atari Breakout
- master (top hierarchy level) agent = DDPG
- slave (bottom hierarchy level) agent = DQN
A custom block factory implementation should look as follows:
class CustomFactory(BlockFactory):
def __init__(self, custom_params):
super().__init__()
def _create_block(self, task_index: int, device=None) -> BlockScheduler:
"""
Create all the block modules and the block scheduler
:param task_index: the index of the process on which the worker will be run
:return: the initialized block scheduler
"""
# Create env
# Create composite agents
# Create level managers
# Create block scheduler
return block_scheduler