Update README.md

README.md

@@ -1,35 +1,37 @@
----
-library_name: hivex
-original_train_name: DroneBasedReforestation_difficulty_10_task_5_run_id_1_train
-tags:
-- hivex
-- hivex-drone-based-reforestation
-- reinforcement-learning
-- multi-agent-reinforcement-learning
-model-index:
-- name: hivex-DBR-PPO-baseline-task-5-difficulty-10
-  results:
-  - task:
-      type: sub-task
-      name: find_highest_potential_seed_drop_location
-      task-id: 5
-      difficulty-id: 10
-    dataset:
-      name: hivex-drone-based-reforestation
-      type: hivex-drone-based-reforestation
-    metrics:
-    - type: highest_point_on_terrain_found
-      value: 46.44914810180664 +/- 4.140176869643727
-      name: Highest Point on Terrain Found
-      verified: true
-    - type: out_of_energy_count
-      value: 0.6642222416400909 +/- 0.0670919881726494
-      name: Out of Energy Count
-      verified: true
-    - type: cumulative_reward
-      value: 45.629833755493166 +/- 3.45321067867071
-      name: Cumulative Reward
-      verified: true
----
-
-This model serves as the baseline for the **Drone-Based Reforestation** environment, trained and tested on task <code>5</code> with difficulty <code>10</code> using the Proximal Policy Optimization (PPO) algorithm.<br><br>Environment: **Drone-Based Reforestation**<br>Task: <code>5</code><br>Difficulty: <code>10</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)
+---
+library_name: hivex
+original_train_name: DroneBasedReforestation_difficulty_10_task_5_run_id_1_train
+tags:
+- hivex
+- hivex-drone-based-reforestation
+- reinforcement-learning
+- multi-agent-reinforcement-learning
+model-index:
+- name: hivex-DBR-PPO-baseline-task-5-difficulty-10
+  results:
+  - task:
+      type: sub-task
+      name: find_highest_potential_seed_drop_location
+      task-id: 5
+      difficulty-id: 10
+    dataset:
+      name: hivex-drone-based-reforestation
+      type: hivex-drone-based-reforestation
+    metrics:
+    - type: highest_point_on_terrain_found
+      value: 46.44914810180664 +/- 4.140176869643727
+      name: Highest Point on Terrain Found
+      verified: true
+    - type: out_of_energy_count
+      value: 0.6642222416400909 +/- 0.0670919881726494
+      name: Out of Energy Count
+      verified: true
+    - type: cumulative_reward
+      value: 45.629833755493166 +/- 3.45321067867071
+      name: Cumulative Reward
+      verified: true
+---
+
+This model serves as the baseline for the **Drone-Based Reforestation** environment, trained and tested on task <code>5</code> with difficulty <code>10</code> using the Proximal Policy Optimization (PPO) algorithm.<br><br>Environment: **Drone-Based Reforestation**<br>Task: <code>5</code><br>Difficulty: <code>10</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)
+
+[hivex-paper]: https://arxiv.org/abs/2501.04180