avancement

README.md

@@ -12,4 +12,11 @@ Although, with a bit of luck we end up with a model that reaches the max steps p
 
 
 ### Evaluation
-During evaluation, we get a 100% success rate.
+During evaluation, we get a 100% success rate for 100 trials.
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+
+## Familiarization with a complete RL pipeline: Application to training a robotic arm
+We initialize the 
+
+https://huggingface.co/Thomstr/A2C_CartPole/tree/main
+
+https://wandb.ai/thomasdgr-ecole-centrale-de-lyon/cartpole/runs/vh4anh20/workspace?nw=nwuserthomasdgr
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a2c_sb3_cartpole.py

+from stable_baselines3 import A2C
+from stable_baselines3.common.env_util import make_vec_env
+from huggingface_hub import login
+from huggingface_sb3 import push_to_hub
+import wandb
+
+
+
+
+def play(model):
+    obs = vec_env.reset()
+    while True:
+        action, _states = model.predict(obs)
+        obs, rewards, dones, info = vec_env.step(action)
+        vec_env.render("human")
+
+if __name__ == "__main__":
+    # start a new wandb run to track this script
+    config = {
+        "policy_type": "MlpPolicy",
+        "total_timesteps": 25000,
+        "env_name": "CartPole-v1",
+    }
+    wandb.login(key='4ac81e81b051a56ebfc528b579021cfc9ed1e5dc')
+    run = wandb.init(
+        project="cartpole",
+        config=config,
+        sync_tensorboard=True,
+        monitor_gym=True,
+        save_code=True,
+    )
+    # Parallel environments
+    vec_env = make_vec_env("CartPole-v1", n_envs=4)
+
+    model = A2C("MlpPolicy", vec_env, verbose=1)
+    model.learn(total_timesteps=25000)
+    model.save("a2c_cartpole")
+
+    if False :
+        login(token="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")
+        push_to_hub(
+            repo_id="Thomstr/A2C_CartPole",
+            filename="a2c_cartpole.zip",
+            commit_message="Added A2C model for CartPole with Stable Baselines3",
+        )
+
+
+
+

a2c_sb3_panda_reach.py

+import gym
+import panda_gym
+from stable_baselines3 import A2C
+from stable_baselines3.common.monitor import Monitor
+from stable_baselines3.common.vec_env import DummyVecEnv
+import wandb
+from wandb.integration.sb3 import WandbCallback
+
+
+config = {
+    "policy_type": "MultiInputPolicy",
+    "total_timesteps": 500000,
+    "env_name": "PandaReachJointsDense-v3",
+}
+
+run = wandb.init(
+    project="pandareach",
+    config=config,
+    sync_tensorboard=True,
+    monitor_gym=True,
+    save_code=True,
+)
+
+def make_env():
+    env = gym.make(config["env_name"])
+    env = Monitor(env)  # record stats such as returns
+    return env
+
+env = DummyVecEnv([make_env])
+env = gym.make("PandaReachJointsDense-v3")
+model = A2C(config["policy_type"], env, verbose=1, tensorboard_log=f"runs/{run.id}")
+model.learn(
+    total_timesteps=config["total_timesteps"],
+    callback=WandbCallback(
+    )
+)
+
+run.finish()
+
+login(token="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")
+push_to_hub(
+            repo_id="Thomstr/A2C_PandaReach",
+            filename="a2c_pandareach.zip",
+            commit_message="Added A2C model for PandaReach with Stable Baselines3",
+        )
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