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| from typing import Dict, List | |
| from mlagents_envs.base_env import BaseEnv, BehaviorName, BehaviorSpec | |
| from mlagents.trainers.env_manager import EnvManager, EnvironmentStep, AllStepResult | |
| from mlagents_envs.timers import timed | |
| from mlagents.trainers.action_info import ActionInfo | |
| from mlagents.trainers.settings import ParameterRandomizationSettings | |
| from mlagents_envs.side_channel.environment_parameters_channel import ( | |
| EnvironmentParametersChannel, | |
| ) | |
| class SimpleEnvManager(EnvManager): | |
| """ | |
| Simple implementation of the EnvManager interface that only handles one BaseEnv at a time. | |
| This is generally only useful for testing; see SubprocessEnvManager for a production-quality implementation. | |
| """ | |
| def __init__(self, env: BaseEnv, env_params: EnvironmentParametersChannel): | |
| super().__init__() | |
| self.env_params = env_params | |
| self.env = env | |
| self.previous_step: EnvironmentStep = EnvironmentStep.empty(0) | |
| self.previous_all_action_info: Dict[str, ActionInfo] = {} | |
| def _step(self) -> List[EnvironmentStep]: | |
| all_action_info = self._take_step(self.previous_step) | |
| self.previous_all_action_info = all_action_info | |
| for brain_name, action_info in all_action_info.items(): | |
| self.env.set_actions(brain_name, action_info.env_action) | |
| self.env.step() | |
| all_step_result = self._generate_all_results() | |
| step_info = EnvironmentStep( | |
| all_step_result, 0, self.previous_all_action_info, {} | |
| ) | |
| self.previous_step = step_info | |
| return [step_info] | |
| def _reset_env( | |
| self, config: Dict[BehaviorName, float] = None | |
| ) -> List[EnvironmentStep]: # type: ignore | |
| self.set_env_parameters(config) | |
| self.env.reset() | |
| all_step_result = self._generate_all_results() | |
| self.previous_step = EnvironmentStep(all_step_result, 0, {}, {}) | |
| return [self.previous_step] | |
| def set_env_parameters(self, config: Dict = None) -> None: | |
| """ | |
| Sends environment parameter settings to C# via the | |
| EnvironmentParametersSidehannel. | |
| :param config: Dict of environment parameter keys and values | |
| """ | |
| if config is not None: | |
| for k, v in config.items(): | |
| if isinstance(v, float): | |
| self.env_params.set_float_parameter(k, v) | |
| elif isinstance(v, ParameterRandomizationSettings): | |
| v.apply(k, self.env_params) | |
| def training_behaviors(self) -> Dict[BehaviorName, BehaviorSpec]: | |
| return self.env.behavior_specs | |
| def close(self): | |
| self.env.close() | |
| def _take_step(self, last_step: EnvironmentStep) -> Dict[BehaviorName, ActionInfo]: | |
| all_action_info: Dict[str, ActionInfo] = {} | |
| for brain_name, step_tuple in last_step.current_all_step_result.items(): | |
| all_action_info[brain_name] = self.policies[brain_name].get_action( | |
| step_tuple[0], | |
| 0, # As there is only one worker, we assign the worker_id to 0. | |
| ) | |
| return all_action_info | |
| def _generate_all_results(self) -> AllStepResult: | |
| all_step_result: AllStepResult = {} | |
| for brain_name in self.env.behavior_specs: | |
| all_step_result[brain_name] = self.env.get_steps(brain_name) | |
| return all_step_result | |