Add RLHF guide and dummy demo with Keras/JAX

examples/rl/README.md

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+# Keras Reinforcement Learning Examples
+
+This directory contains examples demonstrating various Reinforcement Learning (RL) algorithms and techniques implemented using Keras.
+
+## Examples
+
+### RLHF Dummy Demo
+-   **Description**: A simplified demonstration of Reinforcement Learning from Human Feedback (RLHF). It illustrates the core components of RLHF, including a policy model, a reward model, and a training loop that simulates learning from human preferences in a basic environment.
+-   **Python Script**: [`rlhf_dummy_demo.py`](rlhf_dummy_demo.py)
+-   **Guide**: [`md/rlhf_dummy_demo.md`](md/rlhf_dummy_demo.md)
+-   **Notes**: 
+    - This demo uses the JAX backend for Keras.
+    - The accompanying guide explains RLHF concepts and the script's implementation details.
+    - The script encountered timeout issues in some testing environments, which are discussed in the guide.
+
+---
+More examples will be added here.

examples/rl/md/rlhf_dummy_demo.md

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+# Reinforcement Learning from Human Feedback (RLHF) - Dummy Demo Guide
+
+This guide explains the concept of Reinforcement Learning from Human Feedback (RLHF) and walks through the components of the accompanying dummy demo script `rlhf_dummy_demo.py`.
+
+## 1. What is Reinforcement Learning from Human Feedback (RLHF)?
+
+Reinforcement Learning (RL) is a machine learning paradigm where an agent learns to make decisions by interacting with an environment to achieve a goal. The agent receives rewards or penalties based on its actions, and it tries to maximize its cumulative reward over time.
+
+In many real-world scenarios, defining a precise reward function that perfectly captures desired behavior can be extremely challenging. For example, how do you define a reward for "writing a helpful and harmless AI assistant response"? This is where RLHF comes in.
+
+**RLHF** is a technique that incorporates human feedback into the RL process to guide the agent's learning, especially for tasks with complex or hard-to-specify objectives. Instead of relying solely on a pre-defined reward function, RLHF uses human preferences to train a separate "reward model" that learns to predict what kind of behaviors humans prefer. This learned reward model is then used to provide reward signals to the RL agent.
+
+## 2. How RLHF Works (High-Level)
+
+The RLHF process generally involves these key stages:
+
+1.  **Pre-training a Language Model (or Policy Model):**
+    Start with a base model that can generate responses or take actions. For language tasks, this is often a pre-trained language model (LM). This model acts as the initial policy.
+
+2.  **Collecting Human Feedback & Training a Reward Model:**
+    *   Generate multiple outputs (e.g., text responses) from the current policy model for various prompts.
+    *   Present these outputs to human evaluators, who rank them or choose the best one(s) based on desired criteria (e.g., helpfulness, safety, coherence).
+    *   This collected preference data (e.g., "Response A is better than Response B for prompt X") is used to train a separate **reward model**. The reward model takes a prompt and a response (or state-action pair) as input and outputs a scalar score indicating how good that response is according to human preferences.
+
+3.  **Fine-tuning the Policy Model via RL:**
+    *   The pre-trained policy model is then fine-tuned using an RL algorithm (like Proximal Policy Optimization - PPO).
+    *   Instead of using a fixed reward function from the environment, the RL agent receives rewards from the **trained reward model**.
+    *   The agent explores the environment (or generates responses), and the reward model scores these actions/responses. The policy model is updated to produce outputs that the reward model scores highly.
+    *   Often, a constraint (e.g., a KL divergence penalty) is added to prevent the policy from diverging too much from the original pre-trained model, helping to maintain coherence and avoid reward hacking.
+
+This cycle (collecting more data, refining the reward model, and further fine-tuning the policy) can be iterated.
+
+## 3. Walking Through `rlhf_dummy_demo.py`
+
+The `rlhf_dummy_demo.py` script provides a very simplified, "dummy" implementation of these concepts to illustrate the basic mechanics.
+
+**Important Note on Keras Backend:**
+This demo is configured to run with the JAX backend for Keras. This is set at the beginning of the script:
+```python
+import os
+os.environ["KERAS_BACKEND"] = "jax"
+```
+
+### 3.1. The Environment (`SimpleEnvironment`)
+
+The script defines a very basic grid-world like environment where the agent's state is its position on a line.
+```python
+class SimpleEnvironment:
+    def __init__(self, size=3): # Default size is small
+        self.size = size
+        self.state = 0  # Initial state
+
+    def reset(self):
+        self.state = 0
+        return self.state
+
+    def step(self, action):
+        # Simple dynamics: 0 -> left, 1 -> right
+        if action == 0:
+            self.state = max(0, self.state - 1)
+        elif action == 1:
+            self.state = min(self.size - 1, self.state + 1)
+
+        # Reward for reaching the goal (rightmost state)
+        reward = 1 if self.state == self.size - 1 else 0 
+        done = self.state == self.size - 1
+        return self.state, reward, done
+
+    def get_observation_space_shape(self):
+        return (1,) 
+
+    def get_action_space_n(self):
+        return 2 # Two possible actions: left or right
+```
+- The agent can move left or right.
+- It receives a "true" reward of 1 if it reaches the rightmost state (`size - 1`), otherwise 0. This "true" reward is used in the demo to simulate human feedback for training the reward model.
+
+### 3.2. The Policy Model (`create_policy_model`)
+
+This is a simple Keras neural network that takes the current state (observation) as input and outputs probabilities for each action (left/right).
+```python
+import keras_core as keras
+import jax.numpy as jnp
+
+def create_policy_model(observation_space_shape, action_space_n):
+    inputs = keras.Input(shape=observation_space_shape)
+    x = keras.layers.Dense(32, activation="relu")(inputs)
+    outputs = keras.layers.Dense(action_space_n, activation="softmax")(x)
+    model = keras.Model(inputs=inputs, outputs=outputs)
+    return model
+```
+- It's a small Multi-Layer Perceptron (MLP).
+- The `softmax` activation ensures the output represents a probability distribution over actions.
+
+### 3.3. The Reward Model (`create_reward_model`)
+
+This Keras model is designed to predict how "good" a state-action pair is. In a real RLHF setup, this model would be trained on human preference data. In this dummy demo, it's trained using the environment's "true" reward signal as a proxy for human feedback.
+```python
+def create_reward_model(observation_space_shape, action_space_n):
+    # Input is observation + one-hot encoded action
+    inputs = keras.Input(shape=(observation_space_shape[0] + action_space_n,)) 
+    x = keras.layers.Dense(32, activation="relu")(inputs)
+    outputs = keras.layers.Dense(1)(x) # Outputs a scalar reward prediction
+    model = keras.Model(inputs=inputs, outputs=outputs)
+    return model
+```
+- It takes the current state and the chosen action (one-hot encoded) as input.
+- It outputs a single scalar value, representing the predicted reward.
+
+### 3.4. The RLHF Training Loop (`rlhf_training_loop`)
+
+This function contains the core logic for the RLHF process.
+
+```python
+def rlhf_training_loop(env, policy_model, reward_model, num_episodes=10, learning_rate=0.001):
+    policy_optimizer = keras.optimizers.Adam(learning_rate=learning_rate)
+    reward_optimizer = keras.optimizers.Adam(learning_rate=learning_rate)
+
+    # JAX gradient functions using model.stateless_call
+    @jax.jit
+    def policy_loss_fn(policy_model_params, state_input, action, predicted_reward_value_stopped):
+        # ... (calculates policy loss based on predicted_reward_value_stopped)
+        predictions_tuple = policy_model.stateless_call(...)
+        actual_predictions_tensor = predictions_tuple[0]
+        action_probs = actual_predictions_tensor[0]
+        log_prob = jnp.log(action_probs[action] + 1e-7)
+        return -log_prob * predicted_reward_value_stopped
+
+    @jax.jit
+    def reward_loss_fn(reward_model_params, reward_model_input, true_reward_val):
+        # ... (calculates MSE loss between predicted reward and true_reward_val)
+        predictions_tuple = reward_model.stateless_call(...)
+        actual_predictions_tensor = predictions_tuple[0]
+        predicted_reward_val = actual_predictions_tensor[0]
+        loss = keras.losses.mean_squared_error(jnp.array([true_reward_val]), predicted_reward_val)
+        return jnp.mean(loss)
+
+    policy_value_and_grad_fn = jax.jit(jax.value_and_grad(policy_loss_fn, argnums=0))
+    reward_value_and_grad_fn = jax.jit(jax.value_and_grad(reward_loss_fn, argnums=0))
+
+    for episode in range(num_episodes):
+        state = env.reset()
+        # Initialize gradient accumulators
+        policy_grads_accum = [...] 
+        reward_grads_accum = [...]
+        num_steps_in_episode = 0
+
+        while not done:
+            # 1. Get action from policy model
+            action_probs = policy_model(np.array([state]).reshape(1, -1))[0]
+            action = np.random.choice(env.get_action_space_n(), p=np.array(action_probs))
+
+            next_state, true_reward, done = env.step(action) # Environment step
+
+            # 2. Predict reward with reward model
+            #    (Input to reward model: current state + action taken)
+            action_one_hot = jax.nn.one_hot(action, env.get_action_space_n())
+            reward_model_input_np = np.concatenate([...]).reshape(1, -1)
+            predicted_reward_value = reward_model(reward_model_input_np)[0]
+
+            # 3. Reward Model Update (Simulating Human Feedback)
+            #    In this demo, the reward model learns from the "true_reward" from the environment.
+            #    In real RLHF, true_reward would be derived from human preferences.
+            reward_params_dict = {"trainable": reward_model.trainable_variables, ...}
+            current_reward_loss, reward_grads_step = reward_value_and_grad_fn(
+                reward_params_dict, jnp.array(reward_model_input_np), true_reward
+            )
+            # Accumulate reward_grads_step
+
+            # 4. Policy Model Update
+            #    The policy model is updated using the reward predicted by our *reward_model*.
+            stopped_predicted_reward = jax.lax.stop_gradient(predicted_reward_value[0])
+            policy_params_dict = {"trainable": policy_model.trainable_variables, ...}
+            current_policy_loss, policy_grads_step = policy_value_and_grad_fn(
+                policy_params_dict, jnp.array([state]).reshape(1, -1), jnp.array(action), stopped_predicted_reward
+            )
+            # Accumulate policy_grads_step
+
+            state = next_state
+            num_steps_in_episode += 1
+
+        # Apply accumulated gradients at the end of the episode
+        if num_steps_in_episode > 0:
+            avg_policy_grads = [jnp.clip(g / num_steps_in_episode, -1.0, 1.0) ...]
+            policy_optimizer.apply_gradients(zip(avg_policy_grads, policy_model.trainable_variables))
+            # Similarly for reward model gradients...
+
+        if (episode + 1) % 10 == 0: # Reduced print frequency
+            # Print average losses
+            ...
+```
+
+**Key Parts of the Training Loop:**
+
+1.  **Initialization:** Optimizers for both policy and reward models are created. JAX gradient functions (`policy_value_and_grad_fn`, `reward_value_and_grad_fn`) are defined using `jax.value_and_grad` and `model.stateless_call` for compatibility with JAX's functional programming paradigm.
+2.  **Episode Iteration:** The agent interacts with the environment for a set number of episodes.
+3.  **Action Selection:** The policy model determines the action to take in the current state.
+4.  **Environment Interaction:** The agent performs the action and receives the next state, a "true" reward (for training the reward model in this demo), and a done signal.
+5.  **Reward Model Training (Simulated Human Feedback):**
+    *   The reward model predicts a reward for the (state, action) pair.
+    *   Its loss is calculated against the `true_reward` from the environment (this step simulates learning from human preferences where `true_reward` would be derived from human rankings/choices).
+    *   Gradients are computed and accumulated.
+6.  **Policy Model Training:**
+    *   The policy model's loss is calculated. Crucially, this loss is based on the reward predicted by our **current reward model** (not the environment's true reward). This is the core of making the policy learn what the *reward model* thinks is good.
+    *   `jax.lax.stop_gradient` is used on the reward model's prediction when calculating the policy loss. This ensures that the policy update doesn't try to backpropagate gradients *through* the reward model (i.e., the policy takes the current reward model's scores as ground truth for its update).
+    *   Gradients are computed and accumulated.
+7.  **Gradient Application:** At the end of each episode, the accumulated gradients for both the policy and reward models are averaged, clipped (to prevent excessively large updates), and applied using their respective optimizers.
+8.  **Logging:** Periodically, average losses are printed.
+
+## 4. How to Run the Demo
+
+To run the demo, execute the Python script from your terminal:
+
+```bash
+python examples/rl/rlhf_dummy_demo.py
+```
+
+This will:
+1.  Initialize the environment, policy model, and reward model.
+2.  Print summaries of the policy and reward models.
+3.  Start the RLHF training loop for the specified number of episodes (default is 10 in the modified script).
+4.  Print training progress (episode number, total reward, average policy loss, average reward loss).
+5.  After training, it will test the trained policy model for a few steps and print the interactions.
+
+## 5. Note on Current Timeout Issues (Development Context)
+
+During the development and testing of this `rlhf_dummy_demo.py` script in a specific sandboxed environment, persistent timeout issues were encountered. Even with a significantly reduced environment size (`size=3`), a small number of episodes (`num_episodes=10`), and JIT compilation enabled for JAX functions, the script would often exceed the execution time limit (approx. 6-7 minutes).
+
+The root cause of this extreme slowdown in that particular context was not definitively pinpointed but could be due to:
+*   Specific interactions or inefficiencies within the Keras/JAX stack (`model.stateless_call`, `jax.grad`, optimizer updates) for this setup.
+*   Severe performance limitations of the testing sandbox.
+*   Subtle JAX JIT recompilation issues triggered by type or shape inconsistencies that were not fully resolved.
+
+The script, as provided, represents the logical structure of a dummy RLHF loop. If you encounter similar performance issues in your environment, further profiling and investigation specific to your JAX/Keras versions and hardware would be necessary. For typical local machine execution, 10 episodes of this simple demo should complete very quickly.