Simple Tag

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

Import	from pettingzoo.mpe import simple_tag_v3
Actions	Discrete/Continuous
Parallel API	Yes
Manual Control	No
Agents	agents= [adversary_0, adversary_1, adversary_2, agent_0]
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)

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

API

class pettingzoo.mpe.simple_tag.simple_tag.raw_env(num_good=1, num_adversaries=3, num_obstacles=2, max_cycles=25, continuous_actions=False, render_mode=None)

Uses the args and kwargs from the object’s constructor for pickling.

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]