Create app.py

app.py

@@ -0,0 +1,129 @@
+import gradio as gr
+import numpy as np
+import pandas as pd
+import matplotlib.pyplot as plt
+from minimal_self_full import MinimalSelf, MovingObstacle, SocialEntity, run_simulation, compute_phi
+
+plt.switch_backend("Agg")  # Ensure headless plotting
+
+def run_agent(
+    steps=500,
+    epsilon=0.2,
+    learning_rate=0.1,
+    reward_type="original",
+    obstacle=False,
+    social=False,
+    seed=123
+):
+    # Configure agent
+    agent = MinimalSelf(
+        seed=seed,
+        epsilon=epsilon,
+        learning_rate=learning_rate,
+        body_bit_reinforce_factor=0.1,
+        body_bit_decay_rate=0.01,
+        reward_type=reward_type
+    )
+
+    # Optional entities
+    entity_actions = [
+        np.array([0, 1]), np.array([1, 0]), np.array([0, -1]), np.array([-1, 0])
+    ]
+    obs = None
+    soc = None
+    if obstacle:
+        obs = MovingObstacle(start_pos=np.array([0, 0]), actions=entity_actions, seed=43)
+    if social:
+        soc = SocialEntity(start_pos=np.array([2, 2]), actions=entity_actions, seed=44)
+
+    # Run simulation
+    history = run_simulation(agent, steps, obstacle_instance=obs, social_entity_instance=soc)
+    df = pd.DataFrame(history)
+
+    # Compute final phi
+    final_phi = compute_phi(history)
+
+    # Plot metrics
+    fig1, axes = plt.subplots(4, 1, figsize=(10, 9), sharex=True)
+    metrics = ["predictive_rate", "C_min", "body_bit_strength", "reward"]
+    for i, m in enumerate(metrics):
+        if m in df.columns:
+            axes[i].plot(df["t"], df[m], label=m, color=["#2b8","#06c","#a5a","#e67"][i])
+            axes[i].set_ylabel(m)
+            axes[i].grid(True)
+            axes[i].legend()
+        else:
+            axes[i].text(0.5, 0.5, f"{m} not available", transform=axes[i].transAxes, ha="center")
+    axes[-1].set_xlabel("Time step")
+    fig1.suptitle("Metrics over time")
+    fig1.tight_layout()
+
+    # Plot path (agent + optional obstacle/social)
+    fig2, ax = plt.subplots(figsize=(6, 6))
+    ax.set_title("Agent and environment paths")
+    ax.set_xlabel("X")
+    ax.set_ylabel("Y")
+    ax.set_aspect("equal", adjustable="box")
+    ax.grid(True)
+    ax.set_xticks(np.arange(0, 3))
+    ax.set_yticks(np.arange(0, 3))
+    ax.set_xlim(-0.5, 2.5)
+    ax.set_ylim(-0.5, 2.5)
+
+    # Agent path
+    ax.plot([p[0] for p in df["position"]], [p[1] for p in df["position"]],
+            marker="o", linestyle="-", color="blue", alpha=0.7, label="Agent")
+    ax.scatter(df["position"].iloc[0][0], df["position"].iloc[0][1], color="cyan", s=80, label="Start")
+    ax.scatter(df["position"].iloc[-1][0], df["position"].iloc[-1][1], color="navy", s=80, label="End")
+
+    # Obstacle path
+    if obstacle and "obstacle_position" in df.columns:
+        ax.plot([p[0] for p in df["obstacle_position"]], [p[1] for p in df["obstacle_position"]],
+                marker="x", linestyle="--", color="red", alpha=0.6, label="Obstacle")
+
+    # Social entity path
+    if social and "social_entity_position" in df.columns:
+        ax.plot([p[0] for p in df["social_entity_position"]], [p[1] for p in df["social_entity_position"]],
+                marker="^", linestyle=":", color="green", alpha=0.6, label="Social entity")
+
+    ax.legend()
+
+    # Prepare CSV
+    csv_bytes = df.to_csv(index=False).encode("utf-8")
+
+    return (
+        gr.Plot(fig1),
+        gr.Plot(fig2),
+        f"{final_phi:.2f}",
+        csv_bytes
+    )
+
+with gr.Blocks(title="RFT Minimal Self: 3×3 Agent") as demo:
+    gr.Markdown("# RFT Minimal Self: 3×3 Agent")
+    gr.Markdown("Run the 3×3 embodied agent with Q-learning, obstacles, and social mimicry. Visualize metrics, paths, and export results.")
+
+    with gr.Row():
+        steps = gr.Slider(100, 5000, value=500, step=50, label="Steps")
+        epsilon = gr.Slider(0.0, 1.0, value=0.2, step=0.05, label="Epsilon (exploration)")
+        learning_rate = gr.Slider(0.0, 1.0, value=0.1, step=0.05, label="Learning rate")
+        seed = gr.Number(value=123, label="Seed")
+
+    reward_type = gr.Radio(choices=["original", "explore_grow", "social"], value="original", label="Reward type")
+    obstacle = gr.Checkbox(value=False, label="Enable moving obstacle")
+    social = gr.Checkbox(value=False, label="Enable social entity")
+
+    run_btn = gr.Button("Run simulation")
+
+    metrics_plot = gr.Plot(label="Metrics over time")
+    path_plot = gr.Plot(label="Paths in 3×3 world")
+    final_phi = gr.Textbox(label="Final Φ_min (toy measure)", interactive=False)
+    csv_out = gr.File(label="Download results.csv", file_count="single")
+
+    run_btn.click(
+        fn=run_agent,
+        inputs=[steps, epsilon, learning_rate, reward_type, obstacle, social, seed],
+        outputs=[metrics_plot, path_plot, final_phi, csv_out]
+    )
+
+if __name__ == "__main__":
+    demo.launch()