Simple Tag¶
This environment is part of the MPE environments. Please read that page first for general information.
Import |
|
|---|---|
Actions |
Discrete/Continuous |
Parallel API |
Yes |
Manual Control |
No |
Agents |
|
Agents |
4 |
Action Shape |
(5) |
Action Values |
Discrete(5)/Box(0.0, 1.0, (50)) |
Observation Shape |
(14),(16) |
Observation Values |
(-inf,inf) |
State Shape |
(62,) |
State Values |
(-inf,inf) |
This is a predator-prey environment. Good agents (green) are faster and receive a negative reward for being hit by adversaries (red) (-10 for each collision). Adversaries are slower and are rewarded for hitting good agents (+10 for each collision). Obstacles (large black circles) block the way. By default, there is 1 good agent, 3 adversaries and 2 obstacles.
So that good agents don’t run to infinity, they are also penalized for exiting the area by the following function:
def bound(x):
if x < 0.9:
return 0
if x < 1.0:
return (x - 0.9) * 10
return min(np.exp(2 * x - 2), 10)
Agent and adversary observations: [self_vel, self_pos, landmark_rel_positions, other_agent_rel_positions, other_agent_velocities]
Agent and adversary action space: [no_action, move_left, move_right, move_down, move_up]
Arguments¶
simple_tag_v3.env(num_good=1, num_adversaries=3, num_obstacles=2, max_cycles=25, continuous_actions=False, dynamic_rescaling=False)
num_good: number of good agents
num_adversaries: number of adversaries
num_obstacles: number of obstacles
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
Usage¶
AEC¶
from pettingzoo.mpe import simple_tag_v3
env = simple_tag_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_tag_v3
env = simple_tag_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()