This environment is part of the mpe environments. Please read that page first for general information.
|Action Values||Discrete(5)/Box(0.0, 1.0, (50))|
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]
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