From 20cf6277c31ed47c20e85fe173d7c7b7fa004c2c Mon Sep 17 00:00:00 2001
From: Chauvin Hugo <hugo.chauvin@etu.ec-lyon.fr>
Date: Tue, 5 Mar 2024 22:33:18 +0000
Subject: [PATCH] Upload New File
---
a2c_sb3_cartpole.py | 68 +++++++++++++++++++++++++++++++++++++++++++++
1 file changed, 68 insertions(+)
create mode 100644 a2c_sb3_cartpole.py
diff --git a/a2c_sb3_cartpole.py b/a2c_sb3_cartpole.py
new file mode 100644
index 0000000..58b9caa
--- /dev/null
+++ b/a2c_sb3_cartpole.py
@@ -0,0 +1,68 @@
+import wandb, gymnasium as gym
+from stable_baselines3 import A2C
+from stable_baselines3.common.evaluation import evaluate_policy
+from huggingface_hub import hf_api
+from wandb.integration.sb3 import WandbCallback
+
+# Setup the Cartpole environment
+env = gym.make("CartPole-v1", render_mode="rgb_array")
+# Choosing the model
+model = A2C("MlpPolicy", env, verbose=1)
+# Printing initial reward
+reward_before_moy, _ = evaluate_policy(model, env, n_eval_episodes=10)
+print(f"Mean reward before training: {reward_before_moy:.2f}")
+# Model training during 10000 timesteps
+model.learn(total_timesteps=10_000)
+# Printing reward after training
+reward_after_moy, _ = evaluate_policy(model, env, n_eval_episodes=10)
+print(f"Mean reward after training: {reward_after_moy:.2f}")
+
+# Upload and save model
+# Saving the trained model
+model_save_path = "model"
+model.save(model_save_path)
+model_path = "model.zip"
+# Creating repository
+repo_name="BE-RL"
+rep = hf_api.create_repo(token="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", repo_id=repo_name)
+# Uploading model in repository
+repo_id="hchauvin78/BE-RL"
+hf_api.upload_file(token="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", repo_id=repo_id, path_or_fileobj=model_path, path_in_repo=repo_name)
+
+
+# Training with WandB
+# Initializing WandB
+wandb.init(project="cartpole-training", entity="hchauvin78", anonymous="allow")
+
+#Configuring WandB
+config = wandb.config
+config.learning_rate = 0.001
+config.gamma = 0.99
+config.n_steps = 500
+
+#Monitoring model training with WandB
+model = A2C('MlpPolicy', env, verbose=1, tensorboard_log="logs/")
+episode_rewards = []
+
+for i in range(25000):
+ obs = env.reset()[0]
+ reward_tot = 0
+ terminated = False
+
+ while terminated == False:
+ action, _ = model.predict(obs, deterministic=True)
+ obs, reward, terminated, info, _ = env.step(action)
+ reward_tot += reward
+
+ episode_rewards.append(reward_tot)
+ wandb.log({"Episode Reward": reward_tot, "Episode": i})
+ #Log mean reward every 10 episodes
+ if i % 10 == 0:
+ mean_reward = sum(episode_rewards[-10:]) / 10
+ wandb.log({"Mean Reward": mean_reward})
+
+
+#Log final metrics to WandB
+wandb.log({"Mean Reward": mean_reward})
+#Finish WandB run
+wandb.finish()
--
GitLab