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#917 -Finished integrating all agents to either train (policy agents) or evaluate (hard-coded agents). Still some fixing up to do, tidying up, loading etc. also docs. But this is all now working.

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This commit is contained in:
Chris McCarthy
2023-06-20 16:06:55 +01:00
parent 10c94954a5
commit 7b0f47d6f8
16 changed files with 1125 additions and 220 deletions
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19
src/primaite/agents/sb3.py
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@@ -2,12 +2,12 @@ from typing import Optional
import numpy as np
from stable_baselines3 import PPO, A2C
from stable_baselines3.ppo import MlpPolicy as PPOMlp
from primaite import getLogger
from primaite.agents.agent import AgentSessionABC
from primaite.common.enums import RedAgentIdentifier, AgentFramework
from primaite.common.enums import AgentIdentifier, AgentFramework
from primaite.environment.primaite_env import Primaite
from stable_baselines3.ppo import MlpPolicy as PPOMlp
_LOGGER = getLogger(__name__)
@@ -24,13 +24,13 @@ class SB3Agent(AgentSessionABC):
f"got {self._training_config.agent_framework}")
_LOGGER.error(msg)
raise ValueError(msg)
if self._training_config.red_agent_identifier == RedAgentIdentifier.PPO:
if self._training_config.agent_identifier == AgentIdentifier.PPO:
self._agent_class = PPO
elif self._training_config.red_agent_identifier == RedAgentIdentifier.A2C:
elif self._training_config.agent_identifier == AgentIdentifier.A2C:
self._agent_class = A2C
else:
msg = ("Expected PPO or A2C red_agent_identifier, "
f"got {self._training_config.red_agent_identifier.value}")
msg = ("Expected PPO or A2C agent_identifier, "
f"got {self._training_config.agent_identifier.value}")
_LOGGER.error(msg)
raise ValueError(msg)
@@ -40,8 +40,8 @@ class SB3Agent(AgentSessionABC):
_LOGGER.debug(
f"Created {self.__class__.__name__} using: "
f"agent_framework={self._training_config.agent_framework}, "
f"red_agent_identifier="
f"{self._training_config.red_agent_identifier}"
f"agent_identifier="
f"{self._training_config.agent_identifier}"
)
def _setup(self):
@@ -56,7 +56,7 @@ class SB3Agent(AgentSessionABC):
self._agent = self._agent_class(
PPOMlp,
self._env,
verbose=self._training_config.output_verbose_level,
verbose=self.output_verbose_level,
n_steps=self._training_config.num_steps,
tensorboard_log=self._tensorboard_log_path
)
@@ -118,6 +118,7 @@ class SB3Agent(AgentSessionABC):
action = np.int64(action)
obs, rewards, done, info = self._env.step(action)
@classmethod
def load(self):
raise NotImplementedError