SimPyDSS RL Env Test

Steps in an episode created in the cyber-physical RL Environment. The interconnection in blue indicates the communication between the Simpy and Open DSS simulators. 1 indicates the passage of physical-side information to cyber network for determining the packet size. 2 indicates the passage of cyber and physical contingency to each others environment. Currently a physical fault adds an event in cyber emulator to generate a fault information to send to the aggregator. 3 indicates the merge of the cyber and physical state information to feed to the RL algorithm or the Agent. 4 Based on the policy, implement the action by segregating respective action of routing policy and control of sectionalizing switch. 5 Evaluating the goal $G_P$ and $G_C$ for terminating the episode when both goals are reached.

Classes and Functions

Created on Fri July 31 09:17:05 2022

@author: abhijeetsahu

This environment would merge both Simpy and OpenDSS environment.. Current implementation is a dummy merge..Still need to update

class CPEnv_DiscreteDSS_RtrDropRate.CyberPhysicalEnvDummy(cenv, penv, compzones)

Bases: object

CyberPhysical combined RL environment with sequential interaction with each environment

reset()

step(actions)

class CPEnv_DiscreteDSS_RtrDropRate.CyberPhysicalEnvMT(cenv, penv, comp_zones)

Bases: gym.core.Env

CyberPhysical combined RL environment with Multi-Threads

reset()

Resets the environment to an initial state and returns an initial observation.

Note that this function should not reset the environment’s random number generator(s); random variables in the environment’s state should be sampled independently between multiple calls to reset(). In other words, each call of reset() should yield an environment suitable for a new episode, independent of previous episodes.

Returns:

observation (object): the initial observation.

step(actions)

Run one timestep of the environment’s dynamics. When end of episode is reached, you are responsible for calling reset() to reset this environment’s state.

Accepts an action and returns a tuple (observation, reward, done, info).

Args:

action (object): an action provided by the agent

Returns:

observation (object): agent’s observation of the current environment reward (float) : amount of reward returned after previous action done (bool): whether the episode has ended, in which case further step() calls will return undefined results info (dict): contains auxiliary diagnostic information (helpful for debugging, and sometimes learning)

CPEnv_DiscreteDSS_RtrDropRate.CyberPhysicalMapping()