# noqa: D212, D415
"""
# Simple Adversary
```{figure} mpe_simple_adversary.gif
:width: 140px
:name: simple_adversary
```
```{eval-rst}
.. warning::
The environment `pettingzoo.mpe.simple_adversary_v3` has been moved to the new `MPE2 package <https://mpe2.farama.org>`_, and will be removed from PettingZoo in a future release.
Please update your import to `mpe2.simple_adversary_v3`.
```
This environment is part of the <a href='..'>MPE environments</a>. Please read that page first for general information.
| Import | `from pettingzoo.mpe import simple_adversary_v3` |
|--------------------|--------------------------------------------------|
| Actions | Discrete/Continuous |
| Parallel API | Yes |
| Manual Control | No |
| Agents | `agents= [adversary_0, agent_0,agent_1]` |
| Agents | 3 |
| Action Shape | (5) |
| Action Values | Discrete(5)/Box(0.0, 1.0, (5)) |
| Observation Shape | (8),(10) |
| Observation Values | (-inf,inf) |
| State Shape | (28,) |
| State Values | (-inf,inf) |
In this environment, there is 1 adversary (red), N good agents (green), N landmarks (default N=2). All agents observe the position of landmarks and other agents. One landmark is the 'target landmark' (colored green). Good agents are rewarded based on how close the closest one of them is to the
target landmark, but negatively rewarded based on how close the adversary is to the target landmark. The adversary is rewarded based on distance to the target, but it doesn't know which landmark is the target landmark. All rewards are unscaled Euclidean distance (see main MPE documentation for
average distance). This means good agents have to learn to 'split up' and cover all landmarks to deceive the adversary.
Agent observation space: `[goal_rel_position, landmark_rel_position, other_agent_rel_positions]`
Adversary observation space: `[landmark_rel_position, other_agents_rel_positions]`
Agent action space: `[no_action, move_left, move_right, move_down, move_up]`
Adversary action space: `[no_action, move_left, move_right, move_down, move_up]`
### Arguments
``` python
simple_adversary_v3.env(N=2, max_cycles=25, continuous_actions=False, dynamic_rescaling=False)
```
`N`: number of good agents and landmarks
`max_cycles`: number of frames (a step for each agent) until game terminates
`continuous_actions`: Whether agent action spaces are discrete(default) or continuous
`dynamic_rescaling`: Whether to rescale the size of agents and landmarks based on the screen size
"""
import numpy as np
from gymnasium.utils import EzPickle
from pettingzoo.mpe._mpe_utils.core import Agent, Landmark, World
from pettingzoo.mpe._mpe_utils.scenario import BaseScenario
from pettingzoo.mpe._mpe_utils.simple_env import SimpleEnv, make_env
from pettingzoo.utils.conversions import parallel_wrapper_fn
[docs]
class raw_env(SimpleEnv, EzPickle):
def __init__(
self,
N=2,
max_cycles=25,
continuous_actions=False,
render_mode=None,
dynamic_rescaling=False,
):
EzPickle.__init__(
self,
N=N,
max_cycles=max_cycles,
continuous_actions=continuous_actions,
render_mode=render_mode,
)
scenario = Scenario()
world = scenario.make_world(N)
SimpleEnv.__init__(
self,
scenario=scenario,
world=world,
render_mode=render_mode,
max_cycles=max_cycles,
continuous_actions=continuous_actions,
dynamic_rescaling=dynamic_rescaling,
)
self.metadata["name"] = "simple_adversary_v3"
env = make_env(raw_env)
parallel_env = parallel_wrapper_fn(env)
class Scenario(BaseScenario):
def make_world(self, N=2):
world = World()
# set any world properties first
world.dim_c = 2
num_agents = N + 1
world.num_agents = num_agents
num_adversaries = 1
num_landmarks = num_agents - 1
# add agents
world.agents = [Agent() for i in range(num_agents)]
for i, agent in enumerate(world.agents):
agent.adversary = True if i < num_adversaries else False
base_name = "adversary" if agent.adversary else "agent"
base_index = i if i < num_adversaries else i - num_adversaries
agent.name = f"{base_name}_{base_index}"
agent.collide = False
agent.silent = True
agent.size = 0.15
# add landmarks
world.landmarks = [Landmark() for i in range(num_landmarks)]
for i, landmark in enumerate(world.landmarks):
landmark.name = "landmark %d" % i
landmark.collide = False
landmark.movable = False
landmark.size = 0.08
return world
def reset_world(self, world, np_random):
# random properties for agents
world.agents[0].color = np.array([0.85, 0.35, 0.35])
for i in range(1, world.num_agents):
world.agents[i].color = np.array([0.35, 0.35, 0.85])
# random properties for landmarks
for i, landmark in enumerate(world.landmarks):
landmark.color = np.array([0.15, 0.15, 0.15])
# set goal landmark
goal = np_random.choice(world.landmarks)
goal.color = np.array([0.15, 0.65, 0.15])
for agent in world.agents:
agent.goal_a = goal
# set random initial states
for agent in world.agents:
agent.state.p_pos = np_random.uniform(-1, +1, world.dim_p)
agent.state.p_vel = np.zeros(world.dim_p)
agent.state.c = np.zeros(world.dim_c)
for i, landmark in enumerate(world.landmarks):
landmark.state.p_pos = np_random.uniform(-1, +1, world.dim_p)
landmark.state.p_vel = np.zeros(world.dim_p)
def benchmark_data(self, agent, world):
# returns data for benchmarking purposes
if agent.adversary:
return np.sum(np.square(agent.state.p_pos - agent.goal_a.state.p_pos))
else:
dists = []
for lm in world.landmarks:
dists.append(np.sum(np.square(agent.state.p_pos - lm.state.p_pos)))
dists.append(
np.sum(np.square(agent.state.p_pos - agent.goal_a.state.p_pos))
)
return tuple(dists)
# return all agents that are not adversaries
def good_agents(self, world):
return [agent for agent in world.agents if not agent.adversary]
# return all adversarial agents
def adversaries(self, world):
return [agent for agent in world.agents if agent.adversary]
def reward(self, agent, world):
# Agents are rewarded based on minimum agent distance to each landmark
return (
self.adversary_reward(agent, world)
if agent.adversary
else self.agent_reward(agent, world)
)
def agent_reward(self, agent, world):
# Rewarded based on how close any good agent is to the goal landmark, and how far the adversary is from it
shaped_reward = True
shaped_adv_reward = True
# Calculate negative reward for adversary
adversary_agents = self.adversaries(world)
if shaped_adv_reward: # distance-based adversary reward
adv_rew = sum(
np.sqrt(np.sum(np.square(a.state.p_pos - a.goal_a.state.p_pos)))
for a in adversary_agents
)
else: # proximity-based adversary reward (binary)
adv_rew = 0
for a in adversary_agents:
if (
np.sqrt(np.sum(np.square(a.state.p_pos - a.goal_a.state.p_pos)))
< 2 * a.goal_a.size
):
adv_rew -= 5
# Calculate positive reward for agents
good_agents = self.good_agents(world)
if shaped_reward: # distance-based agent reward
pos_rew = -min(
np.sqrt(np.sum(np.square(a.state.p_pos - a.goal_a.state.p_pos)))
for a in good_agents
)
else: # proximity-based agent reward (binary)
pos_rew = 0
if (
min(
np.sqrt(np.sum(np.square(a.state.p_pos - a.goal_a.state.p_pos)))
for a in good_agents
)
< 2 * agent.goal_a.size
):
pos_rew += 5
pos_rew -= min(
np.sqrt(np.sum(np.square(a.state.p_pos - a.goal_a.state.p_pos)))
for a in good_agents
)
return pos_rew + adv_rew
def adversary_reward(self, agent, world):
# Rewarded based on proximity to the goal landmark
shaped_reward = True
if shaped_reward: # distance-based reward
return -np.sqrt(
np.sum(np.square(agent.state.p_pos - agent.goal_a.state.p_pos))
)
else: # proximity-based reward (binary)
adv_rew = 0
if (
np.sqrt(np.sum(np.square(agent.state.p_pos - agent.goal_a.state.p_pos)))
< 2 * agent.goal_a.size
):
adv_rew += 5
return adv_rew
def observation(self, agent, world):
# get positions of all entities in this agent's reference frame
entity_pos = []
for entity in world.landmarks:
entity_pos.append(entity.state.p_pos - agent.state.p_pos)
# entity colors
entity_color = []
for entity in world.landmarks:
entity_color.append(entity.color)
# communication of all other agents
other_pos = []
for other in world.agents:
if other is agent:
continue
other_pos.append(other.state.p_pos - agent.state.p_pos)
if not agent.adversary:
return np.concatenate(
[agent.goal_a.state.p_pos - agent.state.p_pos] + entity_pos + other_pos
)
else:
return np.concatenate(entity_pos + other_pos)
