Create README.md

README.md

@@ -0,0 +1,91 @@
+---
+license: apache-2.0
+tags:
+- jax
+- rl
+- jumanji
+---
+
+# CVRP-V1
+This model is trained on the Jumanji CVRP environment
+
+
+**Developed by:** InstaDeep
+
+### Model Sources
+
+<!-- Provide the basic links for the model. -->
+
+- **Repository:** [Jumanji](https://github.com/instadeepai/jumanji)
+- **Paper:** TBD
+
+### How to use
+
+[Notebook](#)
+
+Go to the jumanji repo for the primary model and requirements. Clone the repo and navigate to the root directory.
+
+```
+pip install -e .
+```
+
+Below is an example script for loading and running the Jumanji model
+
+```python
+import pickle
+import joblib
+
+import jax
+from hydra import compose, initialize
+from huggingface_hub import hf_hub_download
+
+
+from jumanji.training.setup_train import setup_agent, setup_env
+from jumanji.training.utils import first_from_device
+
+# initialise the config
+with initialize(version_base=None, config_path="jumanji/training/configs"):
+    cfg = compose(config_name="config.yaml", overrides=["env=cvrp", "agent=a2c"])
+
+# get model state from HF
+REPO_ID = "InstaDeepAI/jumanji-cvrp-v1-a2c-benchmark"
+FILENAME = "CVRP-v1_training_state"
+
+model_weights = hf_hub_download(repo_id=REPO_ID, filename=FILENAME)
+
+with open(model_weights,"rb") as f:
+    training_state = pickle.load(f)
+
+params = first_from_device(training_state.params_state.params)
+env = setup_env(cfg).unwrapped
+agent = setup_agent(cfg, env)
+policy = jax.jit(agent.make_policy(params.actor, stochastic = False))
+
+# rollout a few episodes
+NUM_EPISODES = 10
+
+states = []
+key = jax.random.PRNGKey(cfg.seed)
+for episode in range(NUM_EPISODES):
+    key, reset_key = jax.random.split(key) 
+    state, timestep = jax.jit(env.reset)(reset_key)
+    while not timestep.last():
+        key, action_key = jax.random.split(key)
+        observation = jax.tree_util.tree_map(lambda x: x[None], timestep.observation)
+        action, _ = policy(observation, action_key)
+        state, timestep = jax.jit(env.step)(state, action.squeeze(axis=0))
+        states.append(state)
+    # Freeze the terminal frame to pause the GIF.
+    for _ in range(10):
+        states.append(state)
+
+# animate a GIF
+env.animate(states, interval=150).save("./binpack.gif")
+
+# save PNG
+import matplotlib.pyplot as plt
+%matplotlib inline
+env.render(states[117])
+plt.savefig("connector.png", dpi=300)
+
+```