I am done

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2024-10-30 22:14:35 +01:00
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extends Node2D
class_name AIController2D
enum ControlModes {
INHERIT_FROM_SYNC, ## Inherit setting from sync node
HUMAN, ## Test the environment manually
TRAINING, ## Train a model
ONNX_INFERENCE, ## Load a pretrained model using an .onnx file
RECORD_EXPERT_DEMOS ## Record observations and actions for expert demonstrations
}
@export var control_mode: ControlModes = ControlModes.INHERIT_FROM_SYNC
## The path to a trained .onnx model file to use for inference (overrides the path set in sync node).
@export var onnx_model_path := ""
## Once the number of steps has passed, the flag 'needs_reset' will be set to 'true' for this instance.
@export var reset_after := 1000
@export_group("Record expert demos mode options")
## Path where the demos will be saved. The file can later be used for imitation learning.
@export var expert_demo_save_path: String
## The action that erases the last recorded episode from the currently recorded data.
@export var remove_last_episode_key: InputEvent
## Action will be repeated for n frames. Will introduce control lag if larger than 1.
## Can be used to ensure that action_repeat on inference and training matches
## the recorded demonstrations.
@export var action_repeat: int = 1
@export_group("Multi-policy mode options")
## Allows you to set certain agents to use different policies.
## Changing has no effect with default SB3 training. Works with Rllib example.
## Tutorial: https://github.com/edbeeching/godot_rl_agents/blob/main/docs/TRAINING_MULTIPLE_POLICIES.md
@export var policy_name: String = "shared_policy"
var onnx_model: ONNXModel
var heuristic := "human"
var done := false
var reward := 0.0
var n_steps := 0
var needs_reset := false
var _player: Node2D
func _ready():
add_to_group("AGENT")
func init(player: Node2D):
_player = player
#region Methods that need implementing using the "extend script" option in Godot
func get_obs() -> Dictionary:
assert(false, "the get_obs method is not implemented when extending from ai_controller")
return {"obs": []}
func get_reward() -> float:
assert(false, "the get_reward method is not implemented when extending from ai_controller")
return 0.0
func get_action_space() -> Dictionary:
assert(
false, "the get_action_space method is not implemented when extending from ai_controller"
)
return {
"example_actions_continous": {"size": 2, "action_type": "continuous"},
"example_actions_discrete": {"size": 2, "action_type": "discrete"},
}
func set_action(action) -> void:
assert(false, "the set_action method is not implemented when extending from ai_controller")
#endregion
#region Methods that sometimes need implementing using the "extend script" option in Godot
# Only needed if you are recording expert demos with this AIController
func get_action() -> Array:
assert(
false,
"the get_action method is not implemented in extended AIController but demo_recorder is used"
)
return []
# For providing additional info (e.g. `is_success` for SB3 training)
func get_info() -> Dictionary:
return {}
#endregion
func _physics_process(delta):
n_steps += 1
if n_steps > reset_after:
needs_reset = true
func get_obs_space():
# may need overriding if the obs space is complex
var obs = get_obs()
return {
"obs": {"size": [len(obs["obs"])], "space": "box"},
}
func reset():
n_steps = 0
needs_reset = false
func reset_if_done():
if done:
reset()
func set_heuristic(h):
# sets the heuristic from "human" or "model" nothing to change here
heuristic = h
func get_done():
return done
func set_done_false():
done = false
func zero_reward():
reward = 0.0

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extends Node3D
class_name AIController3D
enum ControlModes {
INHERIT_FROM_SYNC, ## Inherit setting from sync node
HUMAN, ## Test the environment manually
TRAINING, ## Train a model
ONNX_INFERENCE, ## Load a pretrained model using an .onnx file
RECORD_EXPERT_DEMOS ## Record observations and actions for expert demonstrations
}
@export var control_mode: ControlModes = ControlModes.INHERIT_FROM_SYNC
## The path to a trained .onnx model file to use for inference (overrides the path set in sync node).
@export var onnx_model_path := ""
## Once the number of steps has passed, the flag 'needs_reset' will be set to 'true' for this instance.
@export var reset_after := 1000
@export_group("Record expert demos mode options")
## Path where the demos will be saved. The file can later be used for imitation learning.
@export var expert_demo_save_path: String
## The action that erases the last recorded episode from the currently recorded data.
@export var remove_last_episode_key: InputEvent
## Action will be repeated for n frames. Will introduce control lag if larger than 1.
## Can be used to ensure that action_repeat on inference and training matches
## the recorded demonstrations.
@export var action_repeat: int = 1
@export_group("Multi-policy mode options")
## Allows you to set certain agents to use different policies.
## Changing has no effect with default SB3 training. Works with Rllib example.
## Tutorial: https://github.com/edbeeching/godot_rl_agents/blob/main/docs/TRAINING_MULTIPLE_POLICIES.md
@export var policy_name: String = "shared_policy"
var onnx_model: ONNXModel
var heuristic := "human"
var done := false
var reward := 0.0
var n_steps := 0
var needs_reset := false
var _player: Node3D
var move = Vector2.ZERO
func _ready():
add_to_group("AGENT")
func init(player: Node3D):
_player = player
#region Methods that need implementing using the "extend script" option in Godot
func get_obs() -> Dictionary:
return {"obs": []}
func get_reward() -> float:
return reward
func get_action_space() -> Dictionary:
return {
"move": {"size": 2, "action_type": "continuous"},
}
func set_action(action) -> void:
move.x = action["move"][0]
move.y = action["move"][1]
#endregion
#region Methods that sometimes need implementing using the "extend script" option in Godot
# Only needed if you are recording expert demos with this AIController
func get_action() -> Array:
assert(
false,
"the get_action method is not implemented in extended AIController but demo_recorder is used"
)
return []
# For providing additional info (e.g. `is_success` for SB3 training)
func get_info() -> Dictionary:
return {}
#endregion
func _physics_process(delta):
n_steps += 1
if n_steps > reset_after:
needs_reset = true
func get_obs_space():
# may need overriding if the obs space is complex
var obs = get_obs()
return {
"obs": {"size": [len(obs["obs"])], "space": "box"},
}
func reset():
n_steps = 0
needs_reset = false
func reset_if_done():
if done:
reset()
func set_heuristic(h):
# sets the heuristic from "human" or "model" nothing to change here
heuristic = h
func get_done():
return done
func set_done_false():
done = false
func zero_reward():
reward = 0.0