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#917 - Fixed the RLlib integration

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- Dropped support for overriding the num_episodes and num_steps at the agent level. It's just not needed and will add complexity when overriding and writing output files.
This commit is contained in:
Chris McCarthy
2023-06-30 16:52:57 +01:00
parent 00185d3dad
commit e11fd2ced4
43 changed files with 284 additions and 896 deletions
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71
src/primaite/agents/agent.py
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@@ -5,7 +5,7 @@ import time
from abc import ABC, abstractmethod
from datetime import datetime
from pathlib import Path
from typing import Dict, Final, Optional, Union
from typing import Dict, Final, Union
from uuid import uuid4
import yaml
@@ -51,16 +51,12 @@ class AgentSessionABC(ABC):
if not isinstance(training_config_path, Path):
training_config_path = Path(training_config_path)
self._training_config_path: Final[Union[Path]] = training_config_path
self._training_config: Final[TrainingConfig] = training_config.load(
self._training_config_path
)
self._training_config: Final[TrainingConfig] = training_config.load(self._training_config_path)
if not isinstance(lay_down_config_path, Path):
lay_down_config_path = Path(lay_down_config_path)
self._lay_down_config_path: Final[Union[Path]] = lay_down_config_path
self._lay_down_config: Dict = lay_down_config.load(
self._lay_down_config_path
)
self._lay_down_config: Dict = lay_down_config.load(self._lay_down_config_path)
self.output_verbose_level = self._training_config.output_verbose_level
self._env: Primaite
@@ -132,9 +128,7 @@ class AgentSessionABC(ABC):
"learning": {"total_episodes": None, "total_time_steps": None},
"evaluation": {"total_episodes": None, "total_time_steps": None},
"env": {
"training_config": self._training_config.to_dict(
json_serializable=True
),
"training_config": self._training_config.to_dict(json_serializable=True),
"lay_down_config": self._lay_down_config,
},
}
@@ -161,19 +155,11 @@ class AgentSessionABC(ABC):
metadata_dict["end_datetime"] = datetime.now().isoformat()
if not self.is_eval:
metadata_dict["learning"][
"total_episodes"
] = self._env.episode_count # noqa
metadata_dict["learning"][
"total_time_steps"
] = self._env.total_step_count # noqa
metadata_dict["learning"]["total_episodes"] = self._env.episode_count # noqa
metadata_dict["learning"]["total_time_steps"] = self._env.total_step_count # noqa
else:
metadata_dict["evaluation"][
"total_episodes"
] = self._env.episode_count # noqa
metadata_dict["evaluation"][
"total_time_steps"
] = self._env.total_step_count # noqa
metadata_dict["evaluation"]["total_episodes"] = self._env.episode_count # noqa
metadata_dict["evaluation"]["total_time_steps"] = self._env.total_step_count # noqa
filepath = self.session_path / "session_metadata.json"
_LOGGER.debug(f"Updating Session Metadata file: {filepath}")
@@ -184,12 +170,9 @@ class AgentSessionABC(ABC):
@abstractmethod
def _setup(self):
_LOGGER.info(
"Welcome to the Primary-level AI Training Environment "
f"(PrimAITE) (version: {primaite.__version__})"
)
_LOGGER.info(
f"The output directory for this session is: {self.session_path}"
"Welcome to the Primary-level AI Training Environment " f"(PrimAITE) (version: {primaite.__version__})"
)
_LOGGER.info(f"The output directory for this session is: {self.session_path}")
self._write_session_metadata_file()
self._can_learn = True
self._can_evaluate = False
@@ -201,17 +184,11 @@ class AgentSessionABC(ABC):
@abstractmethod
def learn(
self,
time_steps: Optional[int] = None,
episodes: Optional[int] = None,
**kwargs,
):
"""
Train the agent.
:param time_steps: The number of steps per episode. Optional. If not
passed, the value from the training config will be used.
:param episodes: The number of episodes. Optional. If not
passed, the value from the training config will be used.
:param kwargs: Any agent-specific key-word args to be passed.
"""
if self._can_learn:
@@ -225,17 +202,11 @@ class AgentSessionABC(ABC):
@abstractmethod
def evaluate(
self,
time_steps: Optional[int] = None,
episodes: Optional[int] = None,
**kwargs,
):
"""
Evaluate the agent.
:param time_steps: The number of steps per episode. Optional. If not
passed, the value from the training config will be used.
:param episodes: The number of episodes. Optional. If not
passed, the value from the training config will be used.
:param kwargs: Any agent-specific key-word args to be passed.
"""
self._env.set_as_eval() # noqa
@@ -281,9 +252,7 @@ class AgentSessionABC(ABC):
else:
# Session path does not exist
msg = (
f"Failed to load PrimAITE Session, path does not exist: {path}"
)
msg = f"Failed to load PrimAITE Session, path does not exist: {path}"
_LOGGER.error(msg)
raise FileNotFoundError(msg)
pass
@@ -354,17 +323,11 @@ class HardCodedAgentSessionABC(AgentSessionABC):
def learn(
self,
time_steps: Optional[int] = None,
episodes: Optional[int] = None,
**kwargs,
):
"""
Train the agent.
:param time_steps: The number of steps per episode. Optional. If not
passed, the value from the training config will be used.
:param episodes: The number of episodes. Optional. If not
passed, the value from the training config will be used.
:param kwargs: Any agent-specific key-word args to be passed.
"""
_LOGGER.warning("Deterministic agents cannot learn")
@@ -375,27 +338,19 @@ class HardCodedAgentSessionABC(AgentSessionABC):
def evaluate(
self,
time_steps: Optional[int] = None,
episodes: Optional[int] = None,
**kwargs,
):
"""
Evaluate the agent.
:param time_steps: The number of steps per episode. Optional. If not
passed, the value from the training config will be used.
:param episodes: The number of episodes. Optional. If not
passed, the value from the training config will be used.
:param kwargs: Any agent-specific key-word args to be passed.
"""
self._env.set_as_eval() # noqa
self.is_eval = True
if not time_steps:
time_steps = self._training_config.num_steps
time_steps = self._training_config.num_steps
episodes = self._training_config.num_episodes
if not episodes:
episodes = self._training_config.num_episodes
obs = self._env.reset()
for episode in range(episodes):
# Reset env and collect initial observation