csle_agents.agents.c51_clean package

Submodules

csle_agents.agents.c51_clean.c51_clean_agent module

MIT License

Copyright (c) 2019 CleanRL developers https://github.com/vwxyzjn/cleanrl

class csle_agents.agents.c51_clean.c51_clean_agent.C51CleanAgent(simulation_env_config: csle_common.dao.simulation_config.simulation_env_config.SimulationEnvConfig, emulation_env_config: Optional[csle_common.dao.emulation_config.emulation_env_config.EmulationEnvConfig], experiment_config: csle_common.dao.training.experiment_config.ExperimentConfig, 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

A C51 agent using the implementation from CleanRL documentation

hparam_names() → List[str][source]

Gets the hyperparameters

Returns

a list with the hyperparameter names

linear_schedule(duration: int, t: int) → float[source]

Linear exploration rate schedule

Parameters

  • duration – training duration

  • t – the time step of the training

Returns

the updated exploration rate

make_env()[source]

Helper function for creating an environment in training of the agent

Returns

environment creating function

run_c51(exp_result: csle_common.dao.training.experiment_result.ExperimentResult, seed: int) → Tuple[csle_common.dao.training.experiment_result.ExperimentResult, csle_common.dao.simulation_config.base_env.BaseEnv, csle_common.models.q_network.QNetwork][source]

Runs C51 with given seed

Parameters

  • exp_result – the object to save the experiment results

  • seed – the random seed

Retur

the updated experiment results, the environment and the trained model

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

Implements the training logic of the C51 algorithm

Returns

the experiment result

Module contents