csle_agents.agents.reinforce package

Submodules

csle_agents.agents.reinforce.reinforce_agent module

class csle_agents.agents.reinforce.reinforce_agent.ReinforceAgent(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

Reinforce 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

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

a list with the hyperparameter names

reinforce(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 random search 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

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 reinforce

Returns

the training metrics and the trained policies

training_step(saved_rewards: List[List[float]], saved_log_probs: List[List[torch.Tensor]], policy_network: csle_common.models.fnn_w_softmax.FNNwithSoftmax, optimizer: torch.optim.optimizer.Optimizer, gamma: float) → torch.Tensor[source]

Performs a training step of the REINFORCE algorithm

Parameters

  • saved_rewards – list of rewards encountered in the latest episode trajectory

  • saved_log_probs – list of log-action probabilities (log p(a|s)) encountered in the latest episode trajectory

  • policy_network – the policy network

  • optimizer – the optimizer for updating the weights

  • gamma – the discount factor

Returns

loss

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