Upload 13 files
Browse files- Agent.onnx +3 -0
- Agent/Agent-1200561.onnx +3 -0
- Agent/Agent-1200561.pt +3 -0
- Agent/Agent-499678.onnx +3 -0
- Agent/Agent-499678.pt +3 -0
- Agent/Agent-999984.onnx +3 -0
- Agent/Agent-999984.pt +3 -0
- Agent/checkpoint.pt +3 -0
- Agent/events.out.tfevents.1717549944.RICHARD.24476.0 +3 -0
- README.md +59 -0
- configuration.yaml +107 -0
- run_logs/timers.json +575 -0
- run_logs/training_status.json +53 -0
Agent.onnx
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Agent/Agent-1200561.onnx
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Agent/Agent-1200561.pt
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Agent/Agent-499678.onnx
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Agent/Agent-499678.pt
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Agent/Agent-999984.onnx
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Agent/Agent-999984.pt
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Agent/checkpoint.pt
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version https://git-lfs.github.com/spec/v1
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Agent/events.out.tfevents.1717549944.RICHARD.24476.0
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version https://git-lfs.github.com/spec/v1
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README.md
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---
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library_name: hivex
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original_train_name: DroneBasedReforestation_difficulty_2_task_3_run_id_2_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-3-difficulty-2
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results:
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- task:
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type: sub-task
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name: drop_seed
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task-id: 3
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difficulty-id: 2
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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: cumulative_distance_reward
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value: 1.2730848133563994 +/- 0.31384063871152373
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name: Cumulative Distance Reward
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verified: true
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- type: cumulative_distance_until_tree_drop
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value: 46.571126289367676 +/- 6.711804112230181
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name: Cumulative Distance Until Tree Drop
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verified: true
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- type: cumulative_distance_to_existing_trees
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value: 62.378562469482425 +/- 4.8385232941913126
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name: Cumulative Distance to Existing Trees
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verified: true
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- type: cumulative_normalized_distance_until_tree_drop
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value: 0.12730848103761672 +/- 0.03138406355454151
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name: Cumulative Normalized Distance Until Tree Drop
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verified: true
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- type: cumulative_tree_drop_reward
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value: 4.010982251167297 +/- 0.6601700266326962
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name: Cumulative Tree Drop Reward
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verified: true
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- type: out_of_energy_count
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value: 0.05754003098234534 +/- 0.03282736545941193
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name: Out of Energy Count
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verified: true
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- type: recharge_energy_count
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value: 11.035588264465332 +/- 0.725159645414964
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name: Recharge Energy Count
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verified: true
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- type: tree_drop_count
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value: 0.9222618734836578 +/- 0.04192513553044461
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name: Tree Drop Count
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verified: true
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- type: cumulative_reward
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value: 98.53370529174805 +/- 5.198916602319761
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name: Cumulative Reward
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verified: true
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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>3</code> with difficulty <code>2</code> using the Proximal Policy Optimization (PPO) algorithm.<br><br>Environment: **Drone-Based Reforestation**<br>Task: <code>3</code><br>Difficulty: <code>2</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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configuration.yaml
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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: 1024
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buffer_size: 8192
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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: 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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memory: null
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goal_conditioning_type: hyper
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deterministic: false
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learning_rate: 0.0003
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encoding_size: 256
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extrinsic:
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gamma: 0.99
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strength: 0.9
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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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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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66 |
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max_lifetime_restarts: 10
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67 |
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restarts_rate_limit_n: 1
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68 |
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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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82 |
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sampler_parameters:
|
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seed: 5000
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84 |
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value: 2
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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: 3
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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_2_task_3_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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run_logs/timers.json
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