Add Nle env to OpenEnv

examples/nle_random_agent.py

@@ -0,0 +1,126 @@
+#!/usr/bin/env python3
+"""
+Example: Random Agent Playing NetHack via OpenEnv
+
+This script demonstrates how to use the NLE environment through OpenEnv's
+HTTP interface. It runs a random agent for a few episodes.
+
+Prerequisites:
+    1. Build the Docker image:
+       cd src/envs/nle_env/server
+       docker build -t nle-env:latest .
+
+    2. Run this script:
+       python examples/nle_random_agent.py
+"""
+
+import random
+import time
+
+# Add src to path if running directly
+import sys
+from pathlib import Path
+
+src_path = Path(__file__).parent.parent / "src"
+sys.path.insert(0, str(src_path))
+
+from envs.nle_env import NLEEnv, NLEAction
+
+
+def print_stats(observation):
+    """Print human-readable stats from observation."""
+    if observation.blstats is None:
+        return
+
+    blstats = observation.blstats
+    # BLstats indices from NLE documentation
+    print(f"  HP: {blstats[10]}/{blstats[11]}")
+    print(f"  XP Level: {blstats[18]}")
+    print(f"  Gold: {blstats[13]}")
+    print(f"  Dungeon Level: {blstats[12]}")
+
+
+def main():
+    print("=" * 70)
+    print("NLE Random Agent Example")
+    print("=" * 70)
+
+    # Start environment (automatically launches Docker container)
+    print("\n[1/3] Starting NLE environment...")
+    print("(This may take a moment if container needs to start)")
+
+    env = NLEEnv.from_docker_image(
+        "nle-env:latest",
+        # Optional: customize container
+        # env_vars={"NLE_MAX_STEPS": "1000"}
+    )
+
+    print("✓ Environment connected!")
+
+    # Run a few episodes
+    num_episodes = 3
+    max_steps_per_episode = 100
+
+    print(f"\n[2/3] Running {num_episodes} episodes...")
+
+    for episode in range(num_episodes):
+        print(f"\n--- Episode {episode + 1}/{num_episodes} ---")
+
+        # Reset environment
+        result = env.reset()
+        print("Environment reset")
+        print_stats(result.observation)
+
+        episode_reward = 0
+        steps = 0
+
+        # Play episode
+        for step in range(max_steps_per_episode):
+            # Random action (0-112)
+            action = NLEAction(action_id=random.randint(0, 112))
+
+            # Take step
+            result = env.step(action)
+
+            episode_reward += result.reward or 0
+            steps += 1
+
+            # Print occasional updates
+            if step % 20 == 0:
+                print(f"  Step {step}: reward={episode_reward:.1f}")
+
+            # Check if done
+            if result.done:
+                state = env.state()
+                print(f"\nEpisode ended after {steps} steps!")
+                print(f"  Total reward: {episode_reward:.1f}")
+                print(f"  End status: {state.end_status}")
+                print(f"  Final stats:")
+                print_stats(result.observation)
+                break
+        else:
+            print(f"\nReached max steps ({max_steps_per_episode})")
+            print(f"  Total reward: {episode_reward:.1f}")
+
+        time.sleep(0.5)  # Brief pause between episodes
+
+    # Cleanup
+    print("\n[3/3] Cleaning up...")
+    env.close()
+    print("✓ Environment closed")
+
+    print("\n" + "=" * 70)
+    print("Example complete!")
+    print("=" * 70)
+
+
+if __name__ == "__main__":
+    try:
+        main()
+    except KeyboardInterrupt:
+        print("\n\nInterrupted by user")
+    except Exception as e:
+        print(f"\n\nError: {e}")
+        import traceback
+
+        traceback.print_exc()