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_v2.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