Merge branch 'dev' into feature/1386-enable-a-repeatable-or-deterministic-baseline-test
This commit is contained in:
@@ -1,9 +1,11 @@
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from __future__ import annotations
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import json
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import shutil
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from datetime import datetime
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from pathlib import Path
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from typing import Union
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from uuid import uuid4
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from ray.rllib.algorithms import Algorithm
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from ray.rllib.algorithms.a2c import A2CConfig
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@@ -121,9 +123,11 @@ class RLlibAgent(AgentSessionABC):
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def _save_checkpoint(self):
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checkpoint_n = self._training_config.checkpoint_every_n_episodes
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episode_count = self._current_result["episodes_total"]
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if checkpoint_n > 0 and episode_count > 0:
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if (episode_count % checkpoint_n == 0) or (episode_count == self._training_config.num_episodes):
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self._agent.save(str(self.checkpoints_path))
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save_checkpoint = False
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if checkpoint_n:
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save_checkpoint = episode_count % checkpoint_n == 0
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if episode_count and save_checkpoint:
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self._agent.save(str(self.checkpoints_path))
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def learn(
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self,
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@@ -141,6 +145,7 @@ class RLlibAgent(AgentSessionABC):
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for i in range(episodes):
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self._current_result = self._agent.train()
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self._save_checkpoint()
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self.save()
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self._agent.stop()
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super().learn()
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@@ -167,6 +172,26 @@ class RLlibAgent(AgentSessionABC):
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"""Load an agent from file."""
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raise NotImplementedError
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def save(self, overwrite_existing: bool = True):
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"""Save the agent."""
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# Make temp dir to save in isolation
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temp_dir = self.learning_path / str(uuid4())
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temp_dir.mkdir()
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# Save the agent to the temp dir
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self._agent.save(str(temp_dir))
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# Capture the saved Rllib checkpoint inside the temp directory
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for file in temp_dir.iterdir():
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checkpoint_dir = file
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break
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# Zip the folder
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shutil.make_archive(str(self._saved_agent_path).replace(".zip", ""), "zip", checkpoint_dir) # noqa
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# Drop the temp directory
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shutil.rmtree(temp_dir)
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def export(self):
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"""Export the agent to transportable file format."""
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raise NotImplementedError
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