Upload README.md

README.md

@@ -1,70 +1,59 @@
----
-library_name: hivex
-original_train_name: DroneBasedReforestation_difficulty_1_task_0_run_id_2_train
-tags:
-- hivex
-- hivex-drone-based-reforestation
-- reinforcement-learning
-- multi-agent-reinforcement-learning
-model-index:
-- name: hivex-DBR-PPO-baseline-task-0-difficulty-1
-  results:
-  - task:
-      type: main-task
-      name: main_task
-      task-id: 0
-      difficulty-id: 1
-    dataset:
-      name: hivex-drone-based-reforestation
-      type: hivex-drone-based-reforestation
-    metrics:
-    - type: cumulative_distance_reward
-      value: 2.0864201402664184 +/- 0.6296944874797746
-      name: Cumulative Distance Reward
-      verified: true
-    - type: cumulative_distance_until_tree_drop
-      value: 63.19091514587402 +/- 12.303839558575664
-      name: Cumulative Distance Until Tree Drop
-      verified: true
-    - type: cumulative_distance_to_existing_trees
-      value: 61.76650863647461 +/- 13.908253887773586
-      name: Cumulative Distance to Existing Trees
-      verified: true
-    - type: cumulative_normalized_distance_until_tree_drop
-      value: 0.20864201247692107 +/- 0.06296944883377423
-      name: Cumulative Normalized Distance Until Tree Drop
-      verified: true
-    - type: cumulative_tree_drop_reward
-      value: 5.931592869758606 +/- 1.8518746378631161
-      name: Cumulative Tree Drop Reward
-      verified: true
-    - type: out_of_energy_count
-      value: 0.9266984140872956 +/- 0.06184757754397895
-      name: Out of Energy Count
-      verified: true
-    - type: recharge_energy_count
-      value: 10.601777839660645 +/- 1.2478378815502142
-      name: Recharge Energy Count
-      verified: true
-    - type: tree_drop_count
-      value: 1.0418095350265504 +/- 0.08056789785926544
-      name: Tree Drop Count
-      verified: true
-    - type: cumulative_reward
-      value: 8.961133165359497 +/- 2.7381643935331064
-      name: Cumulative Reward
-      verified: true
----
-
-This model serves as the baseline for the Drone-Based Reforestation environment, trained and tested on task 0 with difficulty 1 using the Proximal Policy Optimization (PPO) algorithm.<br>
-<br>
-Environment: Drone-Based Reforestation<br>
-Task: <code>0</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)<br>
+---
+library_name: hivex
+original_train_name: DroneBasedReforestation_difficulty_1_task_0_run_id_2_train
+tags:
+- hivex
+- hivex-drone-based-reforestation
+- reinforcement-learning
+- multi-agent-reinforcement-learning
+model-index:
+- name: hivex-DBR-PPO-baseline-task-0-difficulty-1
+  results:
+  - task:
+      type: main-task
+      name: main_task
+      task-id: 0
+      difficulty-id: 1
+    dataset:
+      name: hivex-drone-based-reforestation
+      type: hivex-drone-based-reforestation
+    metrics:
+    - type: cumulative_distance_reward
+      value: 2.0864201402664184 +/- 0.6296944874797746
+      name: Cumulative Distance Reward
+      verified: true
+    - type: cumulative_distance_until_tree_drop
+      value: 63.19091514587402 +/- 12.303839558575664
+      name: Cumulative Distance Until Tree Drop
+      verified: true
+    - type: cumulative_distance_to_existing_trees
+      value: 61.76650863647461 +/- 13.908253887773586
+      name: Cumulative Distance to Existing Trees
+      verified: true
+    - type: cumulative_normalized_distance_until_tree_drop
+      value: 0.20864201247692107 +/- 0.06296944883377423
+      name: Cumulative Normalized Distance Until Tree Drop
+      verified: true
+    - type: cumulative_tree_drop_reward
+      value: 5.931592869758606 +/- 1.8518746378631161
+      name: Cumulative Tree Drop Reward
+      verified: true
+    - type: out_of_energy_count
+      value: 0.9266984140872956 +/- 0.06184757754397895
+      name: Out of Energy Count
+      verified: true
+    - type: recharge_energy_count
+      value: 10.601777839660645 +/- 1.2478378815502142
+      name: Recharge Energy Count
+      verified: true
+    - type: tree_drop_count
+      value: 1.0418095350265504 +/- 0.08056789785926544
+      name: Tree Drop Count
+      verified: true
+    - type: cumulative_reward
+      value: 8.961133165359497 +/- 2.7381643935331064
+      name: Cumulative Reward
+      verified: true
+---
+
+This model serves as the baseline for the **Drone-Based Reforestation** environment, trained and tested on task <code>0</code> with difficulty <code>1</code> using the Proximal Policy Optimization (PPO) algorithm.<br><br>Environment: **Drone-Based Reforestation**<br>Task: <code>0</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)