# Simple Crypto#

This environment is part of the MPE environments. Please read that page first for general information.

Import

from pettingzoo.mpe import simple_crypto_v3

Actions

Discrete/Continuous

Parallel API

Yes

Manual Control

No

Agents

agents= [eve_0, bob_0, alice_0]

Agents

2

Action Shape

(4)

Action Values

Discrete(4)/Box(0.0, 1.0, (4))

Observation Shape

(4),(8)

Observation Values

(-inf,inf)

State Shape

(20,)

State Values

(-inf,inf)

In this environment, there are 2 good agents (Alice and Bob) and 1 adversary (Eve). Alice must sent a private 1 bit message to Bob over a public channel. Alice and Bob are rewarded +2 if Bob reconstructs the message, but are rewarded -2 if Eve reconstruct the message (that adds to 0 if both teams reconstruct the bit). Eve is rewarded -2 based if it cannot reconstruct the signal, zero if it can. Alice and Bob have a private key (randomly generated at beginning of each episode) which they must learn to use to encrypt the message.

Alice observation space: [message, private_key]

Bob observation space: [private_key, alices_comm]

Eve observation space: [alices_comm]

Alice action space: [say_0, say_1, say_2, say_3]

Bob action space: [say_0, say_1, say_2, say_3]

Eve action space: [say_0, say_1, say_2, say_3]

For Bob and Eve, their communication is checked to be the 1 bit of information that Alice is trying to convey.

## Arguments#

simple_crypto_v3.env(max_cycles=25, continuous_actions=False)


max_cycles: number of frames (a step for each agent) until game terminates

continuous_actions: Whether agent action spaces are discrete(default) or continuous

## Usage#

### AEC#

from pettingzoo.mpe import simple_crypto_v3

env = simple_crypto_v3.env(render_mode="human")
env.reset(seed=42)

for agent in env.agent_iter():
observation, reward, termination, truncation, info = env.last()

if termination or truncation:
action = None
else:
# this is where you would insert your policy
action = env.action_space(agent).sample()

env.step(action)
env.close()



### Parallel#

from pettingzoo.mpe import simple_crypto_v3

env = simple_crypto_v3.parallel_env(render_mode="human")
observations, infos = env.reset()

while env.agents:
# this is where you would insert your policy
actions = {agent: env.action_space(agent).sample() for agent in env.agents}

observations, rewards, terminations, truncations, infos = env.step(actions)
env.close()



## API#

class pettingzoo.mpe.simple_crypto.simple_crypto.raw_env(max_cycles=25, continuous_actions=False, render_mode=None)[source]#
action_spaces: dict[AgentID, gymnasium.spaces.Space]#
agent_selection: AgentID#
agents: list[AgentID]#
infos: dict[AgentID, dict[str, Any]]#
observation_spaces: dict[AgentID, gymnasium.spaces.Space]#
possible_agents: list[AgentID]#
rewards: dict[AgentID, float]#
terminations: dict[AgentID, bool]#
truncations: dict[AgentID, bool]#