csle_agents.agents.differential_evolution package

Submodules

csle_agents.agents.differential_evolution.differential_evolution_agent module

class csle_agents.agents.differential_evolution.differential_evolution_agent.DifferentialEvolutionAgent(simulation_env_config: csle_common.dao.simulation_config.simulation_env_config.SimulationEnvConfig, emulation_env_config: Union[None, csle_common.dao.emulation_config.emulation_env_config.EmulationEnvConfig], experiment_config: csle_common.dao.training.experiment_config.ExperimentConfig, env: Optional[csle_common.dao.simulation_config.base_env.BaseEnv] = None, training_job: Optional[csle_common.dao.jobs.training_job_config.TrainingJobConfig] = None, save_to_metastore: bool = True)[source]

Bases: csle_agents.agents.base.base_agent.BaseAgent

Differential evolution agent

static compute_avg_metrics(metrics: Dict[str, List[Union[float, int]]]) → Dict[str, Union[float, int]][source]

Computes the average metrics of a dict with aggregated metrics

Parameters

metrics – the dict with the aggregated metrics

Returns

the average metrics

differential_evolution(exp_result: csle_common.dao.training.experiment_result.ExperimentResult, seed: int, training_job: csle_common.dao.jobs.training_job_config.TrainingJobConfig, random_seeds: List[int]) → csle_common.dao.training.experiment_result.ExperimentResult[source]

Runs the differential evolution algorithm

Parameters

  • exp_result – the experiment result object to store the result

  • seed – the seed

  • training_job – the training job config

  • random_seeds – list of seeds

Returns

the updated experiment result and the trained policy

ensure_bounds(vec, bounds) → List[float][source]

Utility function to ensure parameter bounds

Parameters

  • vec – the parameter vector

  • bounds – the bounds

Returns

the bounded vector

eval_theta(policy: Union[csle_common.dao.training.multi_threshold_stopping_policy.MultiThresholdStoppingPolicy, csle_common.dao.training.linear_threshold_stopping_policy.LinearThresholdStoppingPolicy], max_steps: int = 200) → Dict[str, Union[float, int]][source]

Evaluates a given threshold policy by running monte-carlo simulations

Parameters

policy – the policy to evaluate

Returns

the average metrics of the evaluation

get_policy(theta: List[float], L: int) → Union[csle_common.dao.training.multi_threshold_stopping_policy.MultiThresholdStoppingPolicy, csle_common.dao.training.linear_threshold_stopping_policy.LinearThresholdStoppingPolicy][source]

Utility method for getting the policy from a parameter vector

Parameters

  • theta – the parameter vector

  • L – the number of parameters

Returns

the policy

hparam_names() → List[str][source]
Returns

a list with the hyperparameter names

static initial_theta(L: int) → numpy.ndarray[Any, numpy.dtype[Any]][source]

Initializes theta randomly

Parameters

L – the dimension of theta

Returns

the initialized theta vector

static round_vec(vec) → List[float][source]

Rounds a vector to 3 decimals

Parameters

vec – the vector to round

Returns

the rounded vector

train() → csle_common.dao.training.experiment_execution.ExperimentExecution[source]

Performs the policy training for the given random seeds using differential evolution

Returns

the training metrics and the trained policies

static update_metrics(metrics: Dict[str, List[Union[float, int]]], info: Dict[str, Union[float, int]]) → Dict[str, List[Union[float, int]]][source]

Update a dict with aggregated metrics using new information from the environment

Parameters

  • metrics – the dict with the aggregated metrics

  • info – the new information

Returns

the updated dict

Module contents