rog_rl package

Submodules

rog_rl.agent module

class rog_rl.agent.DiseaseSimAgent(unique_id, model, prob_agent_movement=0.0, moore=True)

Bases: mesa.agent.Agent

moore = True

move_to(new_position)

Move the agent to a new location on the grid and do other associated house keeping tasks

• Update global observation in model

pos = None

prob_agent_movement = 0.0

process_state_transitions()

random_move()

set_state(new_state: rog_rl.agent_state.AgentState)

step()

A single step of the agent.

trigger_infection(prob_infection=1.0)

Attempts to trigger an infection, and if infection is triggered, then it returns True, else returns False.

rog_rl.agent_event module

class rog_rl.agent_event.AgentEvent(previous_state=<AgentState.SUSCEPTIBLE: 0>, new_state=<AgentState.SUSCEPTIBLE: 0>, update_timestep=-1)

Bases: object

mark_as_executed()

Mark that this event has been executed

mark_as_pending()

Mark that the execution of this event is pending

rog_rl.agent_state module

class rog_rl.agent_state.AgentState

Bases: enum.Enum

An enumeration.

EXPOSED = 1

INFECTIOUS = 2

RECOVERED = 4

SUSCEPTIBLE = 0

SYMPTOMATIC = 3

VACCINATED = 5

rog_rl.benchmark module

rog_rl.benchmark.performance_metrics(render_on=False)

rog_rl.benchmark.profile(filename)

rog_rl.cli module

Console script for rog_rl.

rog_rl.colors module

class rog_rl.colors.ANSI_COLOR_MAP

Bases: object

BACK_BLACK = '\x1b[40m'

BACK_BLUE = '\x1b[44m'

BACK_CYAN = '\x1b[46m'

BACK_GREEN = '\x1b[42m'

BACK_MAGENTA = '\x1b[45m'

BACK_RED = '\x1b[41m'

BACK_RESET = '\x1b[49m'

BACK_WHITE = '\x1b[47m'

BACK_YELLOW = '\x1b[43m'

FORE_BLACK = '\x1b[30m'

FORE_BLUE = '\x1b[34m'

FORE_CYAN = '\x1b[36m'

FORE_GREEN = '\x1b[32m'

FORE_MAGENTA = '\x1b[35m'

FORE_RED = '\x1b[31m'

FORE_RESET = '\x1b[39m'

FORE_WHITE = '\x1b[37m'

FORE_YELLOW = '\x1b[33m'

class rog_rl.colors.ColorMap(mode='rgb')

Bases: object

get_color(d)

class rog_rl.colors.Colors

Bases: object

Reference : https://materialuicolors.co/ # Level : 600

Can potentially use : https://github.com/secretBiology/SecretColors/

AMBER = (255, 179, 0)

BLUE = (30, 136, 229)

BLUE_GREY = (84, 110, 122)

BROWN = (109, 76, 65)

CYAN = (0, 172, 193)

DEEP_ORANGE = (244, 81, 30)

DEEP_PURPLE = (94, 53, 177)

GREEN = (67, 160, 71)

GREY = (117, 117, 117)

INDIGO = (57, 73, 171)

LIGHT_BLUE = (3, 155, 229)

LIGHT_GREEN = (124, 179, 66)

LIGHT_GREY = (234, 237, 237)

LIME = (192, 202, 51)

ORANGE = (251, 140, 0)

PINK = (216, 27, 96)

PURPLE = (142, 36, 170)

RED = (229, 57, 53)

TEAL = (0, 137, 123)

WHITE = (255, 255, 255)

YELLOW = (253, 216, 53)

rog_rl.contact_network module

class rog_rl.contact_network.ContactNetwork

Bases: object

This keeps a record of all the “contacts” that happen in a single simulation

compute_R0()

Returns the value of R0 based on all the registered infections

register_contact(agent_a, agent_b)

register_infection_spread(agent_a, agent_b)

Register the fact that agent_a infected agent_b

rog_rl.disease_planner module

class rog_rl.disease_planner.DiseasePlannerBase(random=False)

Bases: object

This class plans the schedule of different state transitions for a disease

get_disease_plan(base_timestep=0)

Plans out the schedule of the state transitions for a particular agent using a particular disease model.

It returns a list of AgentEvent objects which have to be “executed” by the Agent at the right moment.

sample_disease_progression()

class rog_rl.disease_planner.SEIRDiseasePlanner(latent_period_mu=8, latent_period_sigma=4, incubation_period_mu=20, incubation_period_sigma=12, recovery_period_mu=56, recovery_period_sigma=4, random=False)

Bases: rog_rl.disease_planner.DiseasePlannerBase

This class plans the schedule of different state transitions for a disease

build_disease_plan(disease_progression, base_timestep=0)

get_disease_plan(base_timestep=0)

It returns a list of AgentEvent objects which have to be “executed” by the Agent at the right moment.

sample_disease_progression()

Plans out the schedule of the state transitions for a particular agent using a particular disease model.

class rog_rl.disease_planner.SimpleSEIRDiseasePlanner(latent_period=2, incubation_period=5, recovery_period=14, random=False)

Bases: rog_rl.disease_planner.SEIRDiseasePlanner

This class plans the schedule of different state transitions for a disease

rog_rl.env module

class rog_rl.env.ActionType

Bases: enum.Enum

An enumeration.

STEP = 0

VACCINATE = 1

class rog_rl.env.RogSimEnv(config={})

Bases: gym.core.Env

close()

Override close in your subclass to perform any necessary cleanup.

Environments will automatically close() themselves when garbage collected or when the program exits.

dummy_env_step()

Implements a fake env.step for faster Integration Testing with RL experiments framework

get_current_game_metrics(dummy_simulation=False)

Returns a dictionary containing important game metrics

get_current_game_score()

Returns the current game score

The game score is currently represented as :

(percentage of susceptibles left in the population)

initialize_renderer(mode='human')

render(mode='human')

This methods provides the option to render the environment’s behavior to a window which should be readable to the human eye if mode is set to ‘human’.

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.

seed(seed=None)

Sets the seed for this env’s random number generator(s).

Note:

Some environments use multiple pseudorandom number generators. We want to capture all such seeds used in order to ensure that there aren’t accidental correlations between multiple generators.

Returns:

list<bigint>: Returns the list of seeds used in this env’s random

number generators. The first value in the list should be the “main” seed, or the value which a reproducer should pass to ‘seed’. Often, the main seed equals the provided ‘seed’, but this won’t be true if seed=None, for example.

set_renderer(renderer)

step(action)

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)

update_renderer(mode='human')

Updates the latest board state on the renderer

rog_rl.model module

class rog_rl.model.DiseaseSimModel(width=50, height=50, population_density=0.75, vaccine_density=0, initial_infection_fraction=0.1, initial_vaccination_fraction=0.0, prob_infection=0.2, prob_agent_movement=0.0, disease_planner_config={'incubation_period_mu': 20, 'incubation_period_sigma': 0, 'latent_period_mu': 8, 'latent_period_sigma': 0, 'recovery_period_mu': 56, 'recovery_period_sigma': 0}, max_timesteps=200, early_stopping_patience=14, toric=True, seed=None)

Bases: mesa.model.Model

The model class holds the model-level attributes, manages the agents, and generally handles the global level of our model.

There is only one model-level parameter: how many agents the model contains. When a new model is started, we want it to populate itself with the given number of agents.

The scheduler is a special model component which controls the order in which agents are activated.

get_observation()

get_population_fraction_by_state(state: rog_rl.agent_state.AgentState)

get_scheduler()

initialize_agents(infection_fraction, vaccination_fraction)

Intializes the intial agents on the grid

initialize_contact_network()

Initializes the contact network

initialize_datacollector()

Setup the initial datacollector

initialize_disease_planner()

Initializes a disease planner that the Agents can use to “schedule” infection progressions

initialize_grid()

Initializes the initial Grid

initialize_observation()

Observation is a nd-array of shape (width, height, num_states) where each AgentState will be marked in a separate challenge for each of the cells

initialize_scheduler()

Initializes the scheduler

is_running()

propagate_infections()

Propagates infection during a single simulation step

simulation_completion_checks()

Simulation is complete if :

• if the timesteps have exceeded the number of max_timesteps

or - the fraction of susceptible population is <= 0 or - the fraction of susceptible population has not changed since the last N timesteps

step()

A model step. Used for collecting data and advancing the schedule

tick()

a mirror function for the internal step function to help avoid confusion in the RL codebases (with the RL step)

vaccinate_cell(cell_x, cell_y)

Vaccinates an agent at cell_x, cell_y, if present

Response with : (is_vaccination_successful, vaccination_response) of types (boolean, VaccinationResponse)

rog_rl.renderer module

class rog_rl.renderer.ANSIRenderer

Bases: object

clear_screen()

close()

render(grid)

render_grid(grid)

Renders the Grid in ANSI

render_stats()

setup(mode='ansi')

setup_stats()

update_stats(key, value)

class rog_rl.renderer.Renderer(grid_size=(30, 30))

Bases: object

close()

convert_gym_color(color: rog_rl.colors.Colors)

draw_cell(cell_x, cell_y, color=False)

draw_grid(color)

draw_standard_line(color, start_coord, end_coord)

draw_standard_rect(color, rect_dims)

draw_stats()

get_cell_base(cell_x, cell_y)

get_grid_height()

get_grid_width()

post_render(return_rgb_array=False)

Some part of the code is taken from the file https://github.com/openai/gym/blob/master/gym/envs/classic_control/rendering.py The render method of class viewer clears the window. This also results in any text on the screen to be lost Hence we copy the contents of the render function and modify it

pre_render()

prepare_render()

setup(mode='human')

setup_constants()

setup_stats()

update_stats(key, value)

rog_rl.scheduler module

class rog_rl.scheduler.CustomScheduler(model: mesa.model.Model)

Bases: mesa.time.RandomActivation

add(agent: mesa.agent.Agent) → None

Add an Agent object to the schedule.

Args:

agent: An Agent to be added to the schedule. NOTE: The agent must have a step() method.

get_agent_count_by_state(state: rog_rl.agent_state.AgentState) → int

Returns the current number of agents in a particular state.

get_agent_fraction_by_state(state: rog_rl.agent_state.AgentState) → int

Returns the current number of agents in a particular state.

get_agents_by_state(state: rog_rl.agent_state.AgentState)

remove(agent: mesa.agent.Agent) → None

Remove all instances of a given agent from the schedule.

Args:

agent: An agent object.

update_agent_state_in_registry(agent: mesa.agent.Agent, previous_state: rog_rl.agent_state.AgentState) → None

rog_rl.server module

Configure visualization elements and instantiate a server

rog_rl.server.agent_potrayal(agent)

rog_rl.server.build_server(grid_width=50, grid_height=50)

rog_rl.vaccination_response module

class rog_rl.vaccination_response.VaccinationResponse

Bases: enum.Enum

An enumeration.

AGENT_EXPOSED = 2

AGENT_INFECTIOUS = 3

AGENT_RECOVERED = 5

AGENT_SYMPTOMATIC = 4

AGENT_VACCINATED = 6

AGENT_VACCINES_EXHAUSTED = 7

CELL_EMPTY = 1

VACCINATION_SUCCESS = 0

rog_rl.visualization module

class rog_rl.visualization.CustomTextGrid(grid, converter=None)

Bases: mesa.visualization.TextVisualization.TextGrid

grid = None

render(endl='\n')

What to show when printed.

Module contents

Top-level package for Rog RL.