# noqa: D212, D415
"""
# Simple Push
```{figure} mpe_simple_push.gif
:width: 140px
:name: simple_push
```
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_push_v3` |
|--------------------|---------------------------------------------|
| Actions | Discrete/Continuous |
| Parallel API | Yes |
| Manual Control | No |
| Agents | `agents= [adversary_0, agent_0]` |
| Agents | 2 |
| Action Shape | (5) |
| Action Values | Discrete(5)/Box(0.0, 1.0, (5,)) |
| Observation Shape | (8),(19) |
| Observation Values | (-inf,inf) |
| State Shape | (27,) |
| State Values | (-inf,inf) |
This environment has 1 good agent, 1 adversary, and 1 landmark. The good agent is rewarded based on the distance to the landmark. The adversary is rewarded if it is close to the landmark, and if the agent is far from the landmark (the difference of the distances). Thus the adversary must learn to
push the good agent away from the landmark.
Agent observation space: `[self_vel, goal_rel_position, goal_landmark_id, all_landmark_rel_positions, landmark_ids, other_agent_rel_positions]`
Adversary observation space: `[self_vel, all_landmark_rel_positions, other_agent_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_push_v3.env(max_cycles=25, continuous_actions=False)
```
`max_cycles`: number of frames (a step for each agent) until game terminates
"""
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, max_cycles=25, continuous_actions=False, render_mode=None):
EzPickle.__init__(
self,
max_cycles=max_cycles,
continuous_actions=continuous_actions,
render_mode=render_mode,
)
scenario = Scenario()
world = scenario.make_world()
SimpleEnv.__init__(
self,
scenario=scenario,
world=world,
render_mode=render_mode,
max_cycles=max_cycles,
continuous_actions=continuous_actions,
)
self.metadata["name"] = "simple_push_v3"
env = make_env(raw_env)
parallel_env = parallel_wrapper_fn(env)
class Scenario(BaseScenario):
def make_world(self):
world = World()
# set any world properties first
world.dim_c = 2
num_agents = 2
num_adversaries = 1
num_landmarks = 2
# 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 = True
agent.silent = True
# 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
return world
def reset_world(self, world, np_random):
# random properties for landmarks
for i, landmark in enumerate(world.landmarks):
landmark.color = np.array([0.1, 0.1, 0.1])
landmark.color[i + 1] += 0.8
landmark.index = i
# set goal landmark
goal = np_random.choice(world.landmarks)
for i, agent in enumerate(world.agents):
agent.goal_a = goal
agent.color = np.array([0.25, 0.25, 0.25])
if agent.adversary:
agent.color = np.array([0.75, 0.25, 0.25])
else:
j = goal.index
agent.color[j + 1] += 0.5
# 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 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):
# the distance to the goal
return -np.sqrt(np.sum(np.square(agent.state.p_pos - agent.goal_a.state.p_pos)))
def adversary_reward(self, agent, world):
# keep the nearest good agents away from the goal
agent_dist = [
np.sqrt(np.sum(np.square(a.state.p_pos - a.goal_a.state.p_pos)))
for a in world.agents
if not a.adversary
]
pos_rew = min(agent_dist)
# nearest_agent = world.good_agents[np.argmin(agent_dist)]
# neg_rew = np.sqrt(np.sum(np.square(nearest_agent.state.p_pos - agent.state.p_pos)))
neg_rew = np.sqrt(
np.sum(np.square(agent.goal_a.state.p_pos - agent.state.p_pos))
)
# neg_rew = sum([np.sqrt(np.sum(np.square(a.state.p_pos - agent.state.p_pos))) for a in world.good_agents])
return pos_rew - neg_rew
def observation(self, agent, world):
# get positions of all entities in this agent's reference frame
entity_pos = []
for entity in world.landmarks: # world.entities:
entity_pos.append(entity.state.p_pos - agent.state.p_pos)
# entity colors
entity_color = []
for entity in world.landmarks: # world.entities:
entity_color.append(entity.color)
# communication of all other agents
comm = []
other_pos = []
for other in world.agents:
if other is agent:
continue
comm.append(other.state.c)
other_pos.append(other.state.p_pos - agent.state.p_pos)
if not agent.adversary:
return np.concatenate(
[agent.state.p_vel]
+ [agent.goal_a.state.p_pos - agent.state.p_pos]
+ [agent.color]
+ entity_pos
+ entity_color
+ other_pos
)
else:
return np.concatenate([agent.state.p_vel] + entity_pos + other_pos)
