First push

README.md

@@ -0,0 +1,32 @@
+---
+tags:
+- LunarLander-v2
+- ppo
+- deep-reinforcement-learning
+- reinforcement-learning
+- custom-implementation
+- deep-rl-course
+model-index:
+- name: PPO
+  results:
+  - task:
+      type: reinforcement-learning
+      name: reinforcement-learning
+    dataset:
+      name: LunarLander-v2
+      type: LunarLander-v2
+    metrics:
+    - type: mean_reward
+      value: 104.83 +/- 18.01
+      name: mean_reward
+      verified: false
+---
+
+  # PPO Agent Playing LunarLander-v2
+
+  This is a trained model of a PPO agent playing LunarLander-v2.
+
+  # Hyperparameters
+  See the GitHub for full info and the journey on creating this on the surface not particularly exciting model: https://github.com/MattStammers/PPO_Lander_Implementation 
+
+  It took me 8 attempts to get the score to nearly reach 0 using a cleanRL implementation and WandB metric tracking and then this version was trained after 10 attempts converging at about 3 million training steps

events.out.tfevents.1695069059.rhmmedcatt-ProLiant-ML350-Gen10.23223.0

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:6534fa50be0408aa0878e5d8bb264ea7a140e986a43eac761528e7b199967c45
+size 2448271

model.pt

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:fa3561982d92893028d31ca5cfc8a13c9593138e5475bee965053e2e44f111d1
+size 147301

results.json

@@ -0,0 +1 @@
+{"env_id": "LunarLander-v2", "mean_reward": 104.82761980859839, "std_reward": 18.014334589706, "n_evaluation_episodes": 10, "eval_datetime": "2023-09-19T00:59:32.656971"}