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+# MuZero: DeepMind MCTS (`mctx`) Integration
+
+## Objective  
+Replace custom MCTS logic with `@google-deepmind/mctx.gumbel_muzero_policy`, a stable, optimized Monte Carlo Tree Search implementation used in MuZero-style agents. Designed for latent space search with learned dynamics.
+
+---
+
+## Library  
+- **Name**: `@google-deepmind/mctx`  
+- **Core function**: `gumbel_muzero_policy(...)`  
+- **Backend**: JAX  
+- **Batch support**: Yes  
+- **Gumbel exploration**: Built-in
+
+---
+
+## Function Signatures
+
+### `mctx.gumbel_muzero_policy(...)`
+
+```python
+output = mctx.gumbel_muzero_policy(
+    params=...,
+    rng_key=jax.random.PRNGKey(seed),
+    root_fn=...,
+    recurrent_fn=...,
+    num_simulations=50,
+    max_depth=5,
+    qtransform=mctx.QTransform.FUNNEL,
+)
+```
+
+---
+
+## Required Components
+
+### `root_fn(params, rng_key) → RootFnOutput`
+
+Initial inference at the root node. Must return:
+
+```python
+mctx.RootFnOutput(
+  prior_logits: Float[Array, "B A"],
+  value: Float[Array, "B"],
+  embedding: Float[Array, "B latent_dim"],
+)
+```
+
+- `prior_logits`: raw action logits from the prediction head  
+- `value`: scalar value estimate of the root  
+- `embedding`: latent state output of representation model `h(obs)`
+
+---
+
+### `recurrent_fn(params, state, action) → RecurrentFnOutput`
+
+Transition and prediction step. Must return:
+
+```python
+mctx.RecurrentFnOutput(
+  reward: Float[Array, "B"],
+  value: Float[Array, "B"],
+  embedding: Float[Array, "B latent_dim"],
+  prior_logits: Float[Array, "B A"],
+)
+```
+
+- `reward`: predicted scalar reward from the transition  
+- `value`: predicted value of the resulting state  
+- `embedding`: next latent state from dynamics model  
+- `prior_logits`: predicted action logits at the new state
+
+---
+
+## Outputs from `gumbel_muzero_policy`
+
+```python
+GumbelMuZeroPolicyOutput(
+  action: Int[Array, "B"],
+  logits: Float[Array, "B A"],
+  root_value: Float[Array, "B"],
+)
+```
+
+- `action`: action sampled from the improved search policy  
+- `logits`: improved policy distribution from tree search  
+- `root_value`: predicted value of the current state
+
+---
+
+## Integration Procedure
+
+### 1. Define `root_fn`
+
+```python
+def root_fn(params, rng_key):
+    embedding = model.representation(params, obs)
+    logits, value = model.prediction(params, embedding)
+    return mctx.RootFnOutput(prior_logits=logits, value=value, embedding=embedding)
+```
+
+---
+
+### 2. Define `recurrent_fn`
+
+```python
+def recurrent_fn(params, embedding, action):
+    next_embedding, reward = model.dynamics(params, embedding, action)
+    logits, value = model.prediction(params, next_embedding)
+    return mctx.RecurrentFnOutput(
+        reward=reward,
+        value=value,
+        embedding=next_embedding,
+        prior_logits=logits,
+    )
+```
+
+---
+
+### 3. Replace existing MCTS logic with:
+
+```python
+mcts_output = mctx.gumbel_muzero_policy(
+    params=model_params,
+    rng_key=jax.random.PRNGKey(seed),
+    root_fn=root_fn,
+    recurrent_fn=recurrent_fn,
+    num_simulations=50,
+    max_depth=5,
+)
+```
+
+Extract:
+- `mcts_output.action`: action to execute  
+- `mcts_output.logits`: improved policy (store for training)  
+- `mcts_output.root_value`: value estimate for bootstrapping
+
+---
+
+## Training Notes
+
+- Store `logits` from MCTS, not raw model policy, as training target  
+- Use `root_value` for value loss or bootstrapped targets  
+- Do not backpropagate through `mctx` — only through the model  
+- `reward` and `value` should be scalars per sample  
+- All inputs and outputs must be JAX arrays (`jax.numpy`)
+
+---
+
+## Debugging / Validation
+
+- Ensure all tensor shapes match: `(B,)`, `(B, A)`, `(B, latent_dim)`  
+- Start with 1–2 simulations and verify logit outputs  
+- Validate value estimates are within expected scale  
+- Confirm MCTS logits and selected actions vary with Gumbel noise
+
+---
+
+## Final Checklist
+
+| Task | Complete |
+|------|----------|
+| `representation`, `dynamics`, and `prediction` defined | ☐ |
+| `root_fn` returns logits, value, embedding | ☐ |
+| `recurrent_fn` returns reward, value, next embedding, logits | ☐ |
+| Custom MCTS fully replaced with `mctx.gumbel_muzero_policy` | ☐ |
+| Replay buffer stores search logits and root value | ☐ |
+| Environment compatibility validated | ☐ |
+
+---
+
+## References
+
+- Library: https://github.com/google-deepmind/mctx  
+- Docstring: `help(mctx.gumbel_muzero_policy)` in code  
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