Behavioral Cloning on Re-routing

Classes and Functions

cyber_train_bc_ieee123.evaluate_policy(env, model)

Evaluating the learned policy by running experiments in the environment

Parameters

  • cpenv (Gym.Env) – The SimPy Cyber RL environment

  • model (torch.nn.Module) – Trained policy network model

Returns

average episode length, average reward

Return type

float

cyber_train_bc_ieee123.train_and_evaluate(exp_tajectory_len, channel_bws, router_qlimits, bc_train_epoch)

For different combination of channel bandwidths, router queue limits and expert demonstrations, train and test the policy and saves the results, reward and policy network.

Parameters

  • channel_bws (list) – List of the channel bandwidths value considered in the communication network

  • router_qlimits (list) – List of the router queue upper bound considered in the network

  • bc_train_epoch (int) – The number of epochs the behavioral cloning agent need to be trained

  • exp_tajectory_len (int) – Number of expert demonstration samples to be considered

Returns

Nothing

Return type

None