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hivex-AWS-PPO-baseline-task-5-difficulty-4

Reinforcement Learning
TensorBoard
ONNX
hivex
hivex-aerial-wildfire-suppression
multi-agent-reinforcement-learning
Eval Results (legacy)
Model card Files
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  15. run_logs/training_status.json +62 -0
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+ ---
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+ library_name: hivex
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+ original_train_name: AerialWildfireSuppression_difficulty_4_task_5_run_id_2_train
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+ tags:
5
+ - hivex
6
+ - hivex-aerial-wildfire-suppression
7
+ - reinforcement-learning
8
+ - multi-agent-reinforcement-learning
9
+ model-index:
10
+ - name: hivex-AWS-PPO-baseline-task-5-difficulty-4
11
+ results:
12
+ - task:
13
+ type: sub-task
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+ name: pick_up_water
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+ task-id: 5
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+ difficulty-id: 4
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+ dataset:
18
+ name: hivex-aerial-wildfire-suppression
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+ type: hivex-aerial-wildfire-suppression
20
+ metrics:
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+ - type: water_pickup
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+ value: 0.9976190477609634 +/- 0.010647942115332154
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+ name: Water Pickup
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+ verified: true
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+ - type: cumulative_reward
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+ value: 94.79416007995606 +/- 0.3539865655275776
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+ name: Cumulative Reward
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+ verified: true
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+ ---
30
+
31
+ This model serves as the baseline for the **Aerial Wildfire Suppression** environment, trained and tested on task <code>5</code> with difficulty <code>4</code> using the Proximal Policy Optimization (PPO) algorithm.<br><br>
32
+
33
+ Environment: **Aerial Wildfire Suppression**<br>
34
+ Task: <code>5</code><br>
35
+ Difficulty: <code>4</code><br>
36
+ Algorithm: <code>PPO</code><br>
37
+ Episode Length: <code>3000</code><br>
38
+ Training <code>max_steps</code>: <code>1800000</code><br>
39
+ Testing <code>max_steps</code>: <code>180000</code><br><br>
40
+
41
+ Train & Test [Scripts](https://github.com/hivex-research/hivex)<br>
42
+ Download the [Environment](https://github.com/hivex-research/hivex-environments)
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+ default_settings: null
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+ behaviors:
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+ Agent:
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+ trainer_type: ppo
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+ hyperparameters:
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+ batch_size: 256
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+ buffer_size: 4096
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+ learning_rate: 0.0003
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+ beta: 0.005
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+ epsilon: 0.2
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+ lambd: 0.95
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+ num_epoch: 3
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+ shared_critic: false
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+ learning_rate_schedule: linear
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+ beta_schedule: linear
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+ epsilon_schedule: linear
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+ network_settings:
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+ normalize: false
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+ hidden_units: 256
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+ num_layers: 2
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+ vis_encode_type: simple
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+ memory: null
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+ goal_conditioning_type: hyper
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+ deterministic: false
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+ reward_signals:
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+ extrinsic:
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+ gamma: 0.995
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+ strength: 1.0
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+ network_settings:
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+ normalize: false
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+ hidden_units: 128
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+ num_layers: 2
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+ vis_encode_type: simple
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+ memory: null
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+ goal_conditioning_type: hyper
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+ deterministic: false
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+ init_path: null
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+ keep_checkpoints: 5
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+ checkpoint_interval: 500000
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+ max_steps: 1800000
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+ time_horizon: 4096
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+ summary_freq: 9000
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+ threaded: true
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+ self_play: null
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+ behavioral_cloning: null
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+ env_settings:
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+ env_path: c:/users/pdsie/documents/hivex/src/hivex/training/baseline/ml_agents/dev_environments/Hivex_AerialWildfireSuppression_win
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+ env_args: null
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+ base_port: 5009
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+ num_envs: 1
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+ num_areas: 1
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+ seed: 5000
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+ max_lifetime_restarts: 10
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+ restarts_rate_limit_n: 1
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+ restarts_rate_limit_period_s: 60
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+ engine_settings:
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+ width: 84
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+ height: 84
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+ quality_level: 5
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+ time_scale: 20
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+ target_frame_rate: -1
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+ capture_frame_rate: 60
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+ no_graphics: true
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+ environment_parameters:
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+ difficulty:
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+ curriculum:
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+ - value:
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+ sampler_type: constant
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+ sampler_parameters:
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+ seed: 5000
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+ value: 4
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+ name: difficulty
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+ completion_criteria: null
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+ task:
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+ curriculum:
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+ - value:
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+ sampler_type: constant
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+ sampler_parameters:
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+ seed: 5001
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+ value: 5
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+ name: task
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+ completion_criteria: null
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+ checkpoint_settings:
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+ run_id: AerialWildfireSuppression/train/AerialWildfireSuppression_difficulty_4_task_5_run_id_2_train
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+ initialize_from: null
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+ load_model: false
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+ resume: false
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+ force: false
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+ train_model: false
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+ inference: false
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+ results_dir: results
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+ torch_settings:
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+ device: null
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+ debug: false
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