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fde73ced13
* Remove the use of daemon threads for Redis subscribe. * Emulate act and observe on trainer side to update internal vars.
58 lines
1.9 KiB
Python
58 lines
1.9 KiB
Python
"""
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"""
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import time
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from rl_coach.base_parameters import TaskParameters, DistributedCoachSynchronizationType
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from rl_coach import core_types
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def data_store_ckpt_save(data_store):
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while True:
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data_store.save_to_store()
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time.sleep(10)
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def training_worker(graph_manager, checkpoint_dir):
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"""
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restore a checkpoint then perform rollouts using the restored model
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"""
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# initialize graph
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task_parameters = TaskParameters()
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task_parameters.__dict__['checkpoint_save_dir'] = checkpoint_dir
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task_parameters.__dict__['checkpoint_save_secs'] = 20
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graph_manager.create_graph(task_parameters)
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# save randomly initialized graph
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graph_manager.save_checkpoint()
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# training loop
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steps = 0
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# evaluation offset
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eval_offset = 1
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graph_manager.setup_memory_backend()
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while(steps < graph_manager.improve_steps.num_steps):
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graph_manager.phase = core_types.RunPhase.TRAIN
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graph_manager.fetch_from_worker(num_steps=graph_manager.agent_params.algorithm.num_consecutive_playing_steps.num_steps)
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graph_manager.phase = core_types.RunPhase.UNDEFINED
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if graph_manager.should_train():
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steps += 1
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graph_manager.phase = core_types.RunPhase.TRAIN
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graph_manager.train()
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graph_manager.phase = core_types.RunPhase.UNDEFINED
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if steps * graph_manager.agent_params.algorithm.num_consecutive_playing_steps.num_steps > graph_manager.steps_between_evaluation_periods.num_steps * eval_offset:
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eval_offset += 1
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if graph_manager.evaluate(graph_manager.evaluation_steps):
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break
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if graph_manager.agent_params.algorithm.distributed_coach_synchronization_type == DistributedCoachSynchronizationType.SYNC:
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graph_manager.save_checkpoint()
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else:
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graph_manager.occasionally_save_checkpoint()
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