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)

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]