Update README.md

README.md

@@ -1,39 +1,41 @@
----
-library_name: hivex
-original_train_name: DroneBasedReforestation_difficulty_4_task_6_run_id_1_train
-tags:
-- hivex
-- hivex-drone-based-reforestation
-- reinforcement-learning
-- multi-agent-reinforcement-learning
-model-index:
-- name: hivex-DBR-PPO-baseline-task-6-difficulty-4
-  results:
-  - task:
-      type: sub-task
-      name: explore_furthest_distance_and_return_to_base
-      task-id: 6
-      difficulty-id: 4
-    dataset:
-      name: hivex-drone-based-reforestation
-      type: hivex-drone-based-reforestation
-    metrics:
-    - type: furthest_distance_explored
-      value: 158.60399871826172 +/- 13.606457942958817
-      name: Furthest Distance Explored
-      verified: true
-    - type: out_of_energy_count
-      value: 0.5861587435007095 +/- 0.07528415521708526
-      name: Out of Energy Count
-      verified: true
-    - type: recharge_energy_count
-      value: 127.55600050918758 +/- 115.90250626363267
-      name: Recharge Energy Count
-      verified: true
-    - type: cumulative_reward
-      value: 8.652405815720558 +/- 7.325451196047886
-      name: Cumulative Reward
-      verified: true
----
-
-This model serves as the baseline for the **Drone-Based Reforestation** environment, trained and tested on task <code>6</code> with difficulty <code>4</code> using the Proximal Policy Optimization (PPO) algorithm.<br><br>Environment: **Drone-Based Reforestation**<br>Task: <code>6</code><br>Difficulty: <code>4</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_4_task_6_run_id_1_train
+tags:
+- hivex
+- hivex-drone-based-reforestation
+- reinforcement-learning
+- multi-agent-reinforcement-learning
+model-index:
+- name: hivex-DBR-PPO-baseline-task-6-difficulty-4
+  results:
+  - task:
+      type: sub-task
+      name: explore_furthest_distance_and_return_to_base
+      task-id: 6
+      difficulty-id: 4
+    dataset:
+      name: hivex-drone-based-reforestation
+      type: hivex-drone-based-reforestation
+    metrics:
+    - type: furthest_distance_explored
+      value: 158.60399871826172 +/- 13.606457942958817
+      name: Furthest Distance Explored
+      verified: true
+    - type: out_of_energy_count
+      value: 0.5861587435007095 +/- 0.07528415521708526
+      name: Out of Energy Count
+      verified: true
+    - type: recharge_energy_count
+      value: 127.55600050918758 +/- 115.90250626363267
+      name: Recharge Energy Count
+      verified: true
+    - type: cumulative_reward
+      value: 8.652405815720558 +/- 7.325451196047886
+      name: Cumulative Reward
+      verified: true
+---
+
+This model serves as the baseline for the **Drone-Based Reforestation** environment, trained and tested on task <code>6</code> with difficulty <code>4</code> using the Proximal Policy Optimization (PPO) algorithm.<br><br>Environment: **Drone-Based Reforestation**<br>Task: <code>6</code><br>Difficulty: <code>4</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