| extends Node3D | |
| class_name AIController | |
| var _player : Player | |
| # ------------------ Godot RL Agents Logic ------------------------------------# | |
| var heuristic := "human" | |
| var done := false | |
| # example actions | |
| var movement_action := Vector2(0.0, 0.0) | |
| var look_action := Vector2(0.0, 0.0) | |
| var jump_action := false | |
| var shoot_action := false | |
| var needs_reset := false | |
| var reward := 0.0 | |
| var n_steps_without_positive_reward = 0 | |
| var n_steps = 0 | |
| @onready var wide_raycast_sensor = $WideRaycastSensor | |
| @onready var narrow_raycast_sensor = $NarrowRaycastSensor | |
| func init(player): | |
| _player=player | |
| func set_team(value): | |
| wide_raycast_sensor.team = value | |
| narrow_raycast_sensor.team = value | |
| if value == 0: | |
| wide_raycast_sensor.team_collision_mask = 8 | |
| wide_raycast_sensor.enemy_collision_mask = 16 | |
| narrow_raycast_sensor.team_collision_mask = 8 | |
| narrow_raycast_sensor.enemy_collision_mask = 16 | |
| elif value == 1: | |
| wide_raycast_sensor.team_collision_mask = 16 | |
| wide_raycast_sensor.enemy_collision_mask = 8 | |
| narrow_raycast_sensor.team_collision_mask = 16 | |
| narrow_raycast_sensor.enemy_collision_mask = 8 | |
| func reset(): | |
| n_steps_without_positive_reward = 0 | |
| n_steps = 0 | |
| func reset_if_done(): | |
| if done: | |
| reset() | |
| func get_obs(): | |
| var obs = [] | |
| obs.append_array(wide_raycast_sensor.get_observation()) | |
| obs.append_array(narrow_raycast_sensor.get_observation()) | |
| return { | |
| "obs":obs | |
| } | |
| func get_reward(): | |
| var total_reward = reward + shaping_reward() | |
| if total_reward <= 0.0: | |
| n_steps_without_positive_reward += 1 | |
| else: | |
| n_steps_without_positive_reward -= 1 | |
| n_steps_without_positive_reward = max(0, n_steps_without_positive_reward) | |
| return total_reward | |
| func zero_reward(): | |
| reward = 0.0 | |
| func shaping_reward(): | |
| var s_reward = 0.0 | |
| return s_reward | |
| func set_heuristic(h): | |
| # sets the heuristic from "human" or "model" nothing to change here | |
| heuristic = h | |
| func get_obs_space(): | |
| var obs = get_obs() | |
| return { | |
| "obs": { | |
| "size": [len(obs["obs"])], | |
| "space": "box" | |
| }, | |
| } | |
| func get_action_space(): | |
| return { | |
| "movement_action" : { | |
| "size": 2, | |
| "action_type": "continuous" | |
| }, | |
| "look_action" : { | |
| "size": 2, | |
| "action_type": "continuous" | |
| }, | |
| "jump_action" : { | |
| "size": 2, | |
| "action_type": "discrete" | |
| }, | |
| "shoot_action" : { | |
| "size": 2, | |
| "action_type": "discrete" | |
| }, | |
| } | |
| func get_done(): | |
| return done | |
| func set_done_false(): | |
| done = false | |
| func set_action(action): | |
| movement_action = Vector2(clamp(action["movement_action"][0],-1.0,1.0), clamp(action["movement_action"][1],-1.0,1.0)) | |
| look_action = Vector2(clamp(action["look_action"][0],-1.0,1.0), clamp(action["look_action"][1],-1.0,1.0)) | |
| jump_action = action["jump_action"] == 1 | |
| shoot_action = action["shoot_action"] == 1 | |
| func _physics_process(delta): | |
| n_steps += 1 | |
| if n_steps > 4000: | |
| _player.needs_respawn = true | |