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hivex-research
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hivex-DBR-PPO-baseline-task-1-difficulty-1

Reinforcement Learning
TensorBoard
ONNX
hivex
hivex-drone-based-reforestation
multi-agent-reinforcement-learning
Eval Results
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  10. README.md +31 -0
  11. configuration.yaml +107 -0
  12. run_logs/timers.json +575 -0
  13. run_logs/training_status.json +53 -0
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+ ---
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+ library_name: hivex
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+ original_train_name: DroneBasedReforestation_difficulty_1_task_1_run_id_1_train
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+ tags:
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+ - hivex
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+ - hivex-drone-based-reforestation
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+ - reinforcement-learning
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+ - multi-agent-reinforcement-learning
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+ model-index:
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+ - name: hivex-DBR-PPO-baseline-task-1-difficulty-1
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+ results:
12
+ - task:
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+ type: sub-task
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+ name: find_closest_forest_perimeter
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+ task-id: 1
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+ difficulty-id: 1
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+ dataset:
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+ name: hivex-drone-based-reforestation
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+ type: hivex-drone-based-reforestation
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+ metrics:
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+ - type: out_of_energy_count
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+ value: 0.010309221660718322 +/- 0.013058139169492618
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+ name: Out of Energy Count
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+ verified: true
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+ - type: cumulative_reward
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+ value: 98.335859375 +/- 1.5893231925735067
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+ name: Cumulative Reward
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+ verified: true
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+ ---
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+
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+ This model serves as the baseline for the **Drone-Based Reforestation** environment, trained and tested on task <code>1</code> with difficulty <code>1</code> using the Proximal Policy Optimization (PPO) algorithm.<br><br>Environment: **Drone-Based Reforestation**<br>Task: <code>1</code><br>Difficulty: <code>1</code><br>Algorithm: <code>PPO</code><br>Episode Length: <code>2000</code><br>Training <code>max_steps</code>: <code>1200000</code><br>Testing <code>max_steps</code>: <code>300000</code><br><br>Train & Test [Scripts](https://github.com/hivex-research/hivex)<br>Download the [Environment](https://github.com/hivex-research/hivex-environments)
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+ default_settings: null
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+ trainer_type: ppo
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+ batch_size: 1024
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+ learning_rate: 0.0003
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+ beta: 0.005
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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: 128
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+ num_layers: 2
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+ vis_encode_type: resnet
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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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+ curiosity:
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+ gamma: 0.99
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+ strength: 0.1
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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: resnet
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+ learning_rate: 0.0003
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+ hidden_units: 128
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+ num_layers: 2
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+ vis_encode_type: resnet
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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: 1200000
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+ time_horizon: 8192
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+ summary_freq: 6000
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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_DroneBasedReforestation_win
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+ env_args: null
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+ base_port: 5007
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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: 1
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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: 1
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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: DroneBasedReforestation/train/DroneBasedReforestation_difficulty_1_task_1_run_id_1_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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