csle_agents.agents.reinforce package

Submodules

csle_agents.agents.reinforce.reinforce_agent module

class csle_agents.agents.reinforce.reinforce_agent.ReinforceAgent(simulation_env_config: SimulationEnvConfig, emulation_env_config: Union[None, EmulationEnvConfig], experiment_config: ExperimentConfig, env: Optional[BaseEnv] = None, training_job: Optional[TrainingJobConfig] = None, save_to_metastore: bool = True)[source]

Bases: 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: ExperimentResult, seed: int, training_job: TrainingJobConfig, random_seeds: List[int]) → 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() → 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[Tensor]], policy_network: FNNwithSoftmax, optimizer: Optimizer, gamma: float) → 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