OpenEnv: Production RL Made Simple¶

### *From "Hello World" to RL Training in 5 Minutes* ✨ --- **What if RL environments were as easy to use as REST APIs?** That's OpenEnv. Type-safe. Isolated. Production-ready. 🎯 [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/meta-pytorch/OpenEnv/blob/main/examples/OpenEnv_Tutorial.ipynb) [![GitHub](https://img.shields.io/badge/GitHub-meta--pytorch%2FOpenEnv-blue?logo=github)](https://github.com/meta-pytorch/OpenEnv) [![License](https://img.shields.io/badge/License-BSD%203--Clause-green.svg)](https://opensource.org/licenses/BSD-3-Clause) [![PyTorch](https://img.shields.io/badge/PyTorch-EE4C2C?logo=pytorch&logoColor=white)](https://pytorch.org/) Author: [Sanyam Bhutani](http://twitter.com/bhutanisanyam1/)

Why OpenEnv?¶

Let's take a trip down memory lane:

It's 2016, RL is popular. You read some papers, it looks promising.

But in real world: Cartpole is the best you can run on a gaming GPU.

What do you do beyond Cartpole?

Fast-forward to 2025, GRPO is awesome and this time it's not JUST in theory, it works well in practise and is really here!

The problem still remains, how do you take these RL algorithms and take them beyond Cartpole?

A huge part of RL is giving your algorithms environment access to learn.

We are excited to introduce an Environment Spec for adding Open Environments for RL Training. This will allow you to focus on your experiments and allow everyone to bring their environments.

Focus on experiments, use OpenEnvironments, and build agents that go beyond Cartpole on a single spec.

📋 What You'll Learn¶

🎯 Part 1-2: The Fundamentals - ⚡ RL in 60 seconds - 🤔 Why existing solutions fall short - 💡 The OpenEnv solution	🏗️ Part 3-5: The Architecture - 🔧 How OpenEnv works - 🔍 Exploring real code - 🎮 OpenSpiel integration example
🎮 Part 6-8: Hands-On Demo - 🔌 Use existing OpenSpiel environment - 🤖 Test 4 different policies - 👀 Watch learning happen live	🔧 Part 9-10: Going Further - 🎮 Switch to other OpenSpiel games - ✨ Build your own integration - 🌐 Deploy to production

Pro Tip

This notebook is designed to run top-to-bottom in Google Colab with zero setup!

⏱️ Time: ~5 minutes | 📊 Difficulty: Beginner-friendly | 🎯 Outcome: Production-ready RL knowledge

📑 Table of Contents¶

Part 1: RL in 60 Seconds ⏱️¶

Reinforcement Learning is simpler than you think.

It's just a loop:

while not done:
    observation = environment.observe()
    action = policy.choose(observation)
    reward = environment.step(action)
    policy.learn(reward)

That's it. That's RL.

Let's see it in action:

import random

print("🎲 " + "="*58 + " 🎲")
print("   Number Guessing Game - The Simplest RL Example")
print("🎲 " + "="*58 + " 🎲")

# Environment setup
target = random.randint(1, 10)
guesses_left = 3

print(f"\n🎯 I'm thinking of a number between 1 and 10...")
print(f"💭 You have {guesses_left} guesses. Let's see how random guessing works!\n")

# The RL Loop - Pure random policy (no learning!)
while guesses_left > 0:
    # Policy: Random guessing (no learning yet!)
    guess = random.randint(1, 10)
    guesses_left -= 1

    print(f"💭 Guess #{3-guesses_left}: {guess}", end=" → ")

    # Reward signal (but we're not using it!)
    if guess == target:
        print("🎉 Correct! +10 points")
        break
    elif abs(guess - target) <= 2:
        print("🔥 Warm! (close)")
    else:
        print("❄️  Cold! (far)")
else:
    print(f"\n💔 Out of guesses. The number was {target}.")

print("\n" + "="*62)
print("💡 This is RL: Observe → Act → Reward → Repeat")
print("   But this policy is terrible! It doesn't learn from rewards.")
print("="*62 + "\n")

Output:

🎲 ========================================================== 🎲
   Number Guessing Game - The Simplest RL Example
🎲 ========================================================== 🎲

🎯 I'm thinking of a number between 1 and 10...
💭 You have 3 guesses. Let's see how random guessing works!

💭 Guess #1: 2 → ❄️  Cold! (far)
💭 Guess #2: 10 → 🎉 Correct! +10 points

==============================================================
💡 This is RL: Observe → Act → Reward → Repeat
   But this policy is terrible! It doesn't learn from rewards.
==============================================================

Part 2: The Problem with Traditional RL 😤¶

🤔 Why Can't We Just Use OpenAI Gym?¶

Good question! Gym is great for research, but production needs more...

Challenge	Traditional Approach	OpenEnv Solution
Type Safety	❌ `obs[0][3]` - what is this?	✅ `obs.info_state` - IDE knows!
Isolation	❌ Same process (can crash your training)	✅ Docker containers (fully isolated)
Deployment	❌ "Works on my machine" 🤷	✅ Same container everywhere 🐳
Scaling	❌ Hard to distribute	✅ Deploy to Kubernetes ☸️
Language	❌ Python only	✅ Any language (HTTP API) 🌐
Debugging	❌ Cryptic numpy errors	✅ Clear type errors 🐛

💡 The OpenEnv Philosophy¶

"RL environments should be like microservices"

Think of it like this: You don't run your database in the same process as your web server, right? Same principle!

🔒 Isolated: Run in containers (security + stability)
🌐 Standard: HTTP API, works everywhere
📦 Versioned: Docker images (reproducibility!)
🚀 Scalable: Deploy to cloud with one command
🛡️ Type-safe: Catch bugs before they happen
🔄 Portable: Works on Mac, Linux, Windows, Cloud

The Architecture¶

┌────────────────────────────────────────────────────────────┐
│  YOUR TRAINING CODE                                        │
│                                                            │
│  env = OpenSpielEnv(...)        ← Import the client      │
│  result = env.reset()           ← Type-safe!             │
│  result = env.step(action)      ← Type-safe!             │
│                                                            │
└─────────────────┬──────────────────────────────────────────┘
                  │
                  │  HTTP/JSON (Language-Agnostic)
                  │  POST /reset, POST /step, GET /state
                  │
┌─────────────────▼──────────────────────────────────────────┐
│  DOCKER CONTAINER                                          │
│                                                            │
│  ┌──────────────────────────────────────────────┐         │
│  │  FastAPI Server                              │         │
│  │  └─ Environment (reset, step, state)         │         │
│  │     └─ Your Game/Simulation Logic            │         │
│  └──────────────────────────────────────────────┘         │
│                                                            │
│  Isolated • Reproducible • Secure                          │
└────────────────────────────────────────────────────────────┘

Key Insight

You never see HTTP details - just clean Python methods!

env.reset()    # Under the hood: HTTP POST to /reset
env.step(...)  # Under the hood: HTTP POST to /step
env.state()    # Under the hood: HTTP GET to /state

The magic? OpenEnv handles all the plumbing. You focus on RL! ✨

Part 3: Setup 🛠️¶

Running in Colab? This cell will clone OpenEnv and install dependencies automatically.

Running locally? Make sure you're in the OpenEnv directory.

# Detect environment
try:
    import google.colab
    IN_COLAB = True
    print("🌐 Running in Google Colab - Perfect!")
except ImportError:
    IN_COLAB = False
    print("💻 Running locally - Nice!")

if IN_COLAB:
    print("\n📦 Cloning OpenEnv repository...")
    !git clone https://github.com/meta-pytorch/OpenEnv.git > /dev/null 2>&1
    %cd OpenEnv

    print("📚 Installing dependencies (this takes ~10 seconds)...")
    !pip install -q fastapi uvicorn requests

    import sys
    sys.path.insert(0, './src')
    print("\n✅ Setup complete! Everything is ready to go! 🎉")
else:
    import sys
    from pathlib import Path
    sys.path.insert(0, str(Path.cwd().parent / 'src'))
    print("✅ Using local OpenEnv installation")

print("\n🚀 Ready to explore OpenEnv and build amazing things!")
print("💡 Tip: Run cells top-to-bottom for the best experience.\n")

Output:

💻 Running locally - Nice!
✅ Using local OpenEnv installation

🚀 Ready to explore OpenEnv and build amazing things!
💡 Tip: Run cells top-to-bottom for the best experience.

Part 4: The OpenEnv Pattern 🏗️¶

Every OpenEnv Environment Has 3 Components:¶

src/envs/your_env/
├── 📝 models.py          ← Type-safe contracts
│                           (Action, Observation, State)
│
├── 📱 client.py          ← What YOU import
│                           (HTTPEnvClient implementation)
│
└── 🖥️  server/
    ├── environment.py    ← Game/simulation logic
    ├── app.py            ← FastAPI server
    └── Dockerfile        ← Container definition

Let's explore the actual OpenEnv code to see how this works:

# Import OpenEnv's core abstractions
from core.env_server import Environment, Action, Observation, State
from core.http_env_client import HTTPEnvClient

print("="*70)
print("   🧩 OPENENV CORE ABSTRACTIONS")
print("="*70)

print("""
🖥️  SERVER SIDE (runs in Docker):

    class Environment(ABC):
        '''Base class for all environment implementations'''

        @abstractmethod
        def reset(self) -> Observation:
            '''Start new episode'''

        @abstractmethod
        def step(self, action: Action) -> Observation:
            '''Execute action, return observation'''

        @property
        def state(self) -> State:
            '''Get episode metadata'''

📱 CLIENT SIDE (your training code):

    class HTTPEnvClient(ABC):
        '''Base class for HTTP clients'''

        def reset(self) -> StepResult:
            # HTTP POST /reset

        def step(self, action) -> StepResult:
            # HTTP POST /step

        def state(self) -> State:
            # HTTP GET /state
""")

print("="*70)
print("\n✨ Same interface on both sides - communication via HTTP!")
print("🎯 You focus on RL, OpenEnv handles the infrastructure.\n")

Output:

======================================================================
   🧩 OPENENV CORE ABSTRACTIONS
======================================================================

🖥️  SERVER SIDE (runs in Docker):

    class Environment(ABC):
        '''Base class for all environment implementations'''

        @abstractmethod
        def reset(self) -> Observation:
            '''Start new episode'''

        @abstractmethod
        def step(self, action: Action) -> Observation:
            '''Execute action, return observation'''

        @property
        def state(self) -> State:
            '''Get episode metadata'''

📱 CLIENT SIDE (your training code):

    class HTTPEnvClient(ABC):
        '''Base class for HTTP clients'''

        def reset(self) -> StepResult:
            # HTTP POST /reset

        def step(self, action) -> StepResult:
            # HTTP POST /step

        def state(self) -> State:
            # HTTP GET /state

======================================================================

✨ Same interface on both sides - communication via HTTP!
🎯 You focus on RL, OpenEnv handles the infrastructure.

Part 5: Example Integration - OpenSpiel 🎮¶

What is OpenSpiel?¶

OpenSpiel is a library from DeepMind with 70+ game environments for RL research.

OpenEnv's Integration¶

We've wrapped 6 OpenSpiel games following the OpenEnv pattern:

🎯 Single-Player	👥 Multi-Player
1. Catch - Catch falling ball	5. Tic-Tac-Toe - Classic 3×3
2. Cliff Walking - Navigate grid	6. Kuhn Poker - Imperfect info poker
3. 2048 - Tile puzzle
4. Blackjack - Card game

This shows how OpenEnv can wrap any existing RL library!

from envs.openspiel_env.client import OpenSpielEnv

print("="*70)
print("   🔌 HOW OPENENV WRAPS OPENSPIEL")
print("="*70)

print("""
class OpenSpielEnv(HTTPEnvClient[OpenSpielAction, OpenSpielObservation]):

    def _step_payload(self, action: OpenSpielAction) -> dict:
        '''Convert typed action to JSON for HTTP'''
        return {
            "action_id": action.action_id,
            "game_name": action.game_name,
        }

    def _parse_result(self, payload: dict) -> StepResult:
        '''Parse HTTP JSON response into typed observation'''
        return StepResult(
            observation=OpenSpielObservation(...),
            reward=payload['reward'],
            done=payload['done']
        )

""")

print("─" * 70)
print("\n✨ Usage (works for ALL OpenEnv environments):")
print("""
  env = OpenSpielEnv(base_url="http://localhost:8000")

  result = env.reset()
  # Returns StepResult[OpenSpielObservation] - Type safe!

  result = env.step(OpenSpielAction(action_id=2, game_name="catch"))
  # Type checker knows this is valid!

  state = env.state()
  # Returns OpenSpielState
""")

print("─" * 70)
print("\n🎯 This pattern works for ANY environment you want to wrap!\n")

Output:

======================================================================
   🔌 HOW OPENENV WRAPS OPENSPIEL
======================================================================

class OpenSpielEnv(HTTPEnvClient[OpenSpielAction, OpenSpielObservation]):

    def _step_payload(self, action: OpenSpielAction) -> dict:
        '''Convert typed action to JSON for HTTP'''
        return {
            "action_id": action.action_id,
            "game_name": action.game_name,
        }

    def _parse_result(self, payload: dict) -> StepResult:
        '''Parse HTTP JSON response into typed observation'''
        return StepResult(
            observation=OpenSpielObservation(...),
            reward=payload['reward'],
            done=payload['done']
        )


──────────────────────────────────────────────────────────────────────

✨ Usage (works for ALL OpenEnv environments):

  env = OpenSpielEnv(base_url="http://localhost:8000")

  result = env.reset()
  # Returns StepResult[OpenSpielObservation] - Type safe!

  result = env.step(OpenSpielAction(action_id=2, game_name="catch"))
  # Type checker knows this is valid!

  state = env.state()
  # Returns OpenSpielState

──────────────────────────────────────────────────────────────────────

🎯 This pattern works for ANY environment you want to wrap!

Type-Safe Models¶

# Import OpenSpiel integration models
from envs.openspiel_env.models import (
    OpenSpielAction,
    OpenSpielObservation,
    OpenSpielState
)
from dataclasses import fields

print("="*70)
print("   🎮 OPENSPIEL INTEGRATION - TYPE-SAFE MODELS")
print("="*70)

print("\n📤 OpenSpielAction (what you send):")
print("   " + "─" * 64)
for field in fields(OpenSpielAction):
    print(f"   • {field.name:20s} : {field.type}")

print("\n📥 OpenSpielObservation (what you receive):")
print("   " + "─" * 64)
for field in fields(OpenSpielObservation):
    print(f"   • {field.name:20s} : {field.type}")

print("\n📊 OpenSpielState (episode metadata):")
print("   " + "─" * 64)
for field in fields(OpenSpielState):
    print(f"   • {field.name:20s} : {field.type}")

print("\n" + "="*70)
print("\n💡 Type safety means:")
print("   ✅ Your IDE autocompletes these fields")
print("   ✅ Typos are caught before running")
print("   ✅ Refactoring is safe")
print("   ✅ Self-documenting code\n")

Output:

======================================================================
   🎮 OPENSPIEL INTEGRATION - TYPE-SAFE MODELS
======================================================================

📤 OpenSpielAction (what you send):
   ────────────────────────────────────────────────────────────────
   • metadata             : typing.Dict[str, typing.Any]
   • action_id            : int
   • game_name            : str
   • game_params          : Dict[str, Any]

📥 OpenSpielObservation (what you receive):
   ────────────────────────────────────────────────────────────────
   • done                 : <class 'bool'>
   • reward               : typing.Union[bool, int, float, NoneType]
   • metadata             : typing.Dict[str, typing.Any]
   • info_state           : List[float]
   • legal_actions        : List[int]
   • game_phase           : str
   • current_player_id    : int
   • opponent_last_action : Optional[int]

📊 OpenSpielState (episode metadata):
   ────────────────────────────────────────────────────────────────
   • episode_id           : typing.Optional[str]
   • step_count           : <class 'int'>
   • game_name            : str
   • agent_player         : int
   • opponent_policy      : str
   • game_params          : Dict[str, Any]
   • num_players          : int

======================================================================

💡 Type safety means:
   ✅ Your IDE autocompletes these fields
   ✅ Typos are caught before running
   ✅ Refactoring is safe
   ✅ Self-documenting code

How the Client Works¶

The client inherits from HTTPEnvClient and implements 3 methods:

_step_payload() - Convert action → JSON
_parse_result() - Parse JSON → typed observation
_parse_state() - Parse JSON → state

That's it! The base class handles all HTTP communication.

Part 6: Using Real OpenSpiel 🎮¶

### Now let's USE a production environment! We'll play **Catch** using OpenEnv's **OpenSpiel integration** 🎯 This is a REAL environment running in production at companies! **Get ready for:** - 🔌 Using existing environments (not building) - 🤖 Testing policies against real games - 📊 Live gameplay visualization - 🎯 Production-ready patterns

The Game: Catch 🔴🏓¶

⬜ ⬜ 🔴 ⬜ ⬜
⬜ ⬜ ⬜ ⬜ ⬜
⬜ ⬜ ⬜ ⬜ ⬜   Ball
⬜ ⬜ ⬜ ⬜ ⬜
⬜ ⬜ ⬜ ⬜ ⬜   falls
⬜ ⬜ ⬜ ⬜ ⬜
⬜ ⬜ ⬜ ⬜ ⬜   down
⬜ ⬜ ⬜ ⬜ ⬜
⬜ ⬜ ⬜ ⬜ ⬜
⬜ ⬜ 🏓 ⬜ ⬜
     Paddle

Rules:

10×5 grid
Ball falls from random column
Move paddle left/right to catch it

Actions:

0 = Move LEFT ⬅️
1 = STAY 🛑
2 = Move RIGHT ➡️

Reward:

+1 if caught 🎉
0 if missed 😢

Why Catch?

Simple rules (easy to understand)
Fast episodes (~5 steps)
Clear success/failure
Part of OpenSpiel's 70+ games!

💡 The Big Idea: Instead of building this from scratch, we'll USE OpenEnv's existing OpenSpiel integration. Same interface, but production-ready!

from envs.openspiel_env import OpenSpielEnv
from envs.openspiel_env.models import (
    OpenSpielAction,
    OpenSpielObservation,
    OpenSpielState
)
from dataclasses import fields

print("🎮 " + "="*64 + " 🎮")
print("   ✅ Importing Real OpenSpiel Environment!")
print("🎮 " + "="*64 + " 🎮\n")

print("📦 What we just imported:")
print("   • OpenSpielEnv - HTTP client for OpenSpiel games")
print("   • OpenSpielAction - Type-safe actions")
print("   • OpenSpielObservation - Type-safe observations")
print("   • OpenSpielState - Episode metadata\n")

print("📋 OpenSpielObservation fields:")
print("   " + "─" * 60)
for field in fields(OpenSpielObservation):
    print(f"   • {field.name:25s} : {field.type}")

print("\n" + "="*70)
print("\n💡 This is REAL OpenEnv code - used in production!")
print("   • Wraps 6 OpenSpiel games (Catch, Tic-Tac-Toe, Poker, etc.)")
print("   • Type-safe actions and observations")
print("   • Works via HTTP (we'll see that next!)\n")

Output:

🎮 ================================================================ 🎮
   ✅ Importing Real OpenSpiel Environment!
🎮 ================================================================ 🎮

📦 What we just imported:
   • OpenSpielEnv - HTTP client for OpenSpiel games
   • OpenSpielAction - Type-safe actions
   • OpenSpielObservation - Type-safe observations
   • OpenSpielState - Episode metadata

📋 OpenSpielObservation fields:
   ────────────────────────────────────────────────────────────
   • done                      : <class 'bool'>
   • reward                    : typing.Union[bool, int, float, NoneType]
   • metadata                  : typing.Dict[str, typing.Any]
   • info_state                : List[float]
   • legal_actions             : List[int]
   • game_phase                : str
   • current_player_id         : int
   • opponent_last_action      : Optional[int]

======================================================================

💡 This is REAL OpenEnv code - used in production!
   • Wraps 6 OpenSpiel games (Catch, Tic-Tac-Toe, Poker, etc.)
   • Type-safe actions and observations
   • Works via HTTP (we'll see that next!)

Part 7: Four Policies 🤖¶

Let's test 4 different AI strategies:

Policy	Strategy	Expected Performance
🎲 Random	Pick random action every step	~20% (pure luck)
🛑 Always Stay	Never move, hope ball lands in center	~20% (terrible!)
🧠 Smart	Move paddle toward ball	100% (optimal!)
📈 Learning	Start random, learn smart strategy	~85% (improves over time)

💡 These policies work with ANY OpenSpiel game!

import random

# ============================================================================
# POLICIES - Different AI strategies (adapted for OpenSpiel)
# ============================================================================

class RandomPolicy:
    """Baseline: Pure random guessing."""
    name = "🎲 Random Guesser"

    def select_action(self, obs: OpenSpielObservation) -> int:
        return random.choice(obs.legal_actions)


class AlwaysStayPolicy:
    """Bad strategy: Never moves."""
    name = "🛑 Always Stay"

    def select_action(self, obs: OpenSpielObservation) -> int:
        return 1  # STAY


class SmartPolicy:
    """Optimal: Move paddle toward ball."""
    name = "🧠 Smart Heuristic"

    def select_action(self, obs: OpenSpielObservation) -> int:
        # Parse OpenSpiel observation
        # For Catch: info_state is a flattened 10x5 grid
        # Ball position and paddle position encoded in the vector
        info_state = obs.info_state

        # Find ball and paddle positions from info_state
        # Catch uses a 10x5 grid, so 50 values
        grid_size = 5

        # Find positions (ball = 1.0 in the flattened grid, paddle = 1.0 in the last row of the flattened grid)
        ball_col = None
        paddle_col = None

        for idx, val in enumerate(info_state):
            if abs(val - 1.0) < 0.01:  # Ball
                ball_col = idx % grid_size
                break

        last_row = info_state[-grid_size:]
        paddle_col = last_row.index(1.0) # Paddle

        if ball_col is not None and paddle_col is not None:
            if paddle_col < ball_col:
                return 2  # Move RIGHT
            elif paddle_col > ball_col:
                return 0  # Move LEFT

        return 1  # STAY (fallback)


class LearningPolicy:
    """Simulated RL: Epsilon-greedy exploration."""
    name = "📈 Learning Agent"

    def __init__(self):
        self.steps = 0
        self.smart_policy = SmartPolicy()

    def select_action(self, obs: OpenSpielObservation) -> int:
        self.steps += 1

        # Decay exploration rate over time
        epsilon = max(0.1, 1.0 - (self.steps / 100))

        if random.random() < epsilon:
            # Explore: random action
            return random.choice(obs.legal_actions)
        else:
            # Exploit: use smart strategy
            return self.smart_policy.select_action(obs)


print("🤖 " + "="*64 + " 🤖")
print("   ✅ 4 Policies Created (Adapted for OpenSpiel)!")
print("🤖 " + "="*64 + " 🤖\n")

policies = [RandomPolicy(), AlwaysStayPolicy(), SmartPolicy(), LearningPolicy()]
for i, policy in enumerate(policies, 1):
    print(f"   {i}. {policy.name}")

print("\n💡 These policies work with OpenSpielObservation!")
print("   • Read info_state (flattened grid)")
print("   • Use legal_actions")
print("   • Work with ANY OpenSpiel game that exposes these!\n")

Output:

🤖 ================================================================ 🤖
   ✅ 4 Policies Created (Adapted for OpenSpiel)!
🤖 ================================================================ 🤖

   1. 🎲 Random Guesser
   2. 🛑 Always Stay
   3. 🧠 Smart Heuristic
   4. 📈 Learning Agent

💡 These policies work with OpenSpielObservation!
   • Read info_state (flattened grid)
   • Use legal_actions
   • Work with ANY OpenSpiel game that exposes these!

Part 8: Policy Competition! 🏆¶

Let's run 50 episodes for each policy against REAL OpenSpiel and see who wins!

This is production code - every action is an HTTP call to the OpenSpiel server!

def evaluate_policies(env, num_episodes=50):
    """Compare all policies over many episodes using real OpenSpiel."""
    policies = [
        RandomPolicy(),
        AlwaysStayPolicy(),
        SmartPolicy(),
        LearningPolicy(),
    ]

    print("\n🏆 " + "="*66 + " 🏆")
    print(f"   POLICY SHOWDOWN - {num_episodes} Episodes Each")
    print(f"   Playing against REAL OpenSpiel Catch!")
    print("🏆 " + "="*66 + " 🏆\n")

    results = []
    for policy in policies:
        print(f"⚡ Testing {policy.name}...", end=" ")
        successes = sum(run_episode(env, policy, visualize=False)
                       for _ in range(num_episodes))
        success_rate = (successes / num_episodes) * 100
        results.append((policy.name, success_rate, successes))
        print(f"✓ Done!")

    print("\n" + "="*70)
    print("   📊 FINAL RESULTS")
    print("="*70 + "\n")

    # Sort by success rate (descending)
    results.sort(key=lambda x: x[1], reverse=True)

    # Award medals to top 3
    medals = ["🥇", "🥈", "🥉", "  "]

    for i, (name, rate, successes) in enumerate(results):
        medal = medals[i]
        bar = "█" * int(rate / 2)
        print(f"{medal} {name:25s} [{bar:<50}] {rate:5.1f}% ({successes}/{num_episodes})")

    print("\n" + "="*70)
    print("\n✨ Key Insights:")
    print("   • Random (~20%):      Baseline - pure luck 🎲")
    print("   • Always Stay (~20%): Bad strategy - stays center 🛑")
    print("   • Smart (100%):       Optimal - perfect play! 🧠")
    print("   • Learning (~85%):    Improves over time 📈")
    print("\n🎓 This is Reinforcement Learning + OpenEnv in action:")
    print("   1. We USED existing OpenSpiel environment (didn't build it)")
    print("   2. Type-safe communication over HTTP")
    print("   3. Same code works for ANY OpenSpiel game")
    print("   4. Production-ready architecture\n")

# Run the epic competition!
print("🎮 Starting the showdown against REAL OpenSpiel...\n")
evaluate_policies(client, num_episodes=50)

Part 9: Switching to Other Games 🎮¶

What We Just Used: Real OpenSpiel! 🎉¶

In Parts 6-8, we USED the existing OpenSpiel Catch environment:

What We Did	How It Works
Imported	OpenSpielEnv client (pre-built)
Started	OpenSpiel server via uvicorn
Connected	HTTP client to server
Played	Real OpenSpiel Catch game

🎯 This is production code! Every action was an HTTP call to a real OpenSpiel environment.

🎮 6 Games Available - Same Interface!¶

The beauty of OpenEnv? Same code, different games!

# We just used Catch
env = OpenSpielEnv(base_url="http://localhost:8000")
# game_name="catch" was set via environment variable

# Want Tic-Tac-Toe instead? Just change the game!
# Start server with: OPENSPIEL_GAME=tic_tac_toe uvicorn ...
# Same client code works!

🎮 All 6 Games:

✅ catch - What we just used!
tic_tac_toe - Classic 3×3
kuhn_poker - Imperfect information poker
cliff_walking - Grid navigation
2048 - Tile puzzle
blackjack - Card game

All use the exact same OpenSpielEnv client!

Try Another Game (Optional):¶

# Stop the current server (kill the server_process)
# Then start a new game:

server_process = subprocess.Popen(
    [sys.executable, "-m", "uvicorn",
     "envs.openspiel_env.server.app:app",
     "--host", "0.0.0.0",
     "--port", "8000"],
    env={**os.environ,
         "PYTHONPATH": f"{work_dir}/src",
         "OPENSPIEL_GAME": "tic_tac_toe",  # Changed!
         "OPENSPIEL_AGENT_PLAYER": "0",
         "OPENSPIEL_OPPONENT_POLICY": "random"},
    # ... rest of config
)

# Same client works!
client = OpenSpielEnv(base_url="http://localhost:8000")
result = client.reset()  # Now playing Tic-Tac-Toe!

💡 Key Insight: You don't rebuild anything - you just USE different games with the same client!

Part 10: Create Your Own Integration 🛠️¶

The 5-Step Pattern¶

Want to wrap your own environment in OpenEnv? Here's how:

Step 1: Define Types (`models.py`)¶

from dataclasses import dataclass
from core.env_server import Action, Observation, State

@dataclass
class YourAction(Action):
    action_value: int
    # Add your action fields

@dataclass
class YourObservation(Observation):
    state_data: List[float]
    done: bool
    reward: float
    # Add your observation fields

@dataclass
class YourState(State):
    episode_id: str
    step_count: int
    # Add your state fields

Step 2: Implement Environment (`server/environment.py`)¶

from core.env_server import Environment

class YourEnvironment(Environment):
    def reset(self) -> Observation:
        # Initialize your game/simulation
        return YourObservation(...)

    def step(self, action: Action) -> Observation:
        # Execute action, update state
        return YourObservation(...)

    @property
    def state(self) -> State:
        return self._state

Step 3: Create Client (`client.py`)¶

from core.http_env_client import HTTPEnvClient
from core.types import StepResult

class YourEnv(HTTPEnvClient[YourAction, YourObservation]):
    def _step_payload(self, action: YourAction) -> dict:
        """Convert action to JSON"""
        return {"action_value": action.action_value}

    def _parse_result(self, payload: dict) -> StepResult:
        """Parse JSON to observation"""
        return StepResult(
            observation=YourObservation(...),
            reward=payload['reward'],
            done=payload['done']
        )

    def _parse_state(self, payload: dict) -> YourState:
        return YourState(...)

Step 4: Create Server (`server/app.py`)¶

from core.env_server import create_fastapi_app
from .your_environment import YourEnvironment

env = YourEnvironment()
app = create_fastapi_app(env)

# That's it! OpenEnv creates all endpoints for you.

Step 5: Dockerize (`server/Dockerfile`)¶

FROM python:3.11-slim

WORKDIR /app
COPY requirements.txt .
RUN pip install --no-cache-dir -r requirements.txt

COPY . .
CMD ["uvicorn", "app:app", "--host", "0.0.0.0", "--port", "8000"]

🎓 Examples to Study¶

OpenEnv includes 3 complete examples:

src/envs/echo_env/
Simplest possible environment
Great for testing and learning
src/envs/openspiel_env/
Wraps external library (OpenSpiel)
Shows integration pattern
6 games in one integration
src/envs/coding_env/
Python code execution environment
Shows complex use case
Security considerations

💡 Study these to understand the patterns!

🎓 Summary: Your Journey¶

What You Learned¶

### 📚 Concepts ✅ **RL Fundamentals** - The observe-act-reward loop - What makes good policies - Exploration vs exploitation ✅ **OpenEnv Architecture** - Client-server separation - Type-safe contracts - HTTP communication layer ✅ **Production Patterns** - Docker isolation - API design - Reproducible deployments

### 🛠️ Skills ✅ **Using Environments** - Import OpenEnv clients - Call reset/step/state - Work with typed observations ✅ **Building Environments** - Define type-safe models - Implement Environment class - Create HTTPEnvClient ✅ **Testing & Debugging** - Compare policies - Visualize episodes - Measure performance

OpenEnv vs Traditional RL¶

Feature	Traditional (Gym)	OpenEnv	Winner
Type Safety	❌ Arrays, dicts	✅ Dataclasses	🏆 OpenEnv
Isolation	❌ Same process	✅ Docker	🏆 OpenEnv
Deployment	❌ Manual setup	✅ K8s-ready	🏆 OpenEnv
Language	❌ Python only	✅ Any (HTTP)	🏆 OpenEnv
Reproducibility	❌ "Works on my machine"	✅ Same everywhere	🏆 OpenEnv
Community	✅ Large ecosystem	🟡 Growing	🤝 Both!

The Bottom Line

OpenEnv brings production engineering to RL:

Same environments work locally and in production
Type safety catches bugs early
Docker isolation prevents conflicts
HTTP API works with any language

It's RL for 2024 and beyond.

📚 Resources¶

🔗 Essential Links¶

🏠 OpenEnv GitHub: https://github.com/meta-pytorch/OpenEnv
🎮 OpenSpiel: https://github.com/google-deepmind/open_spiel
⚡ FastAPI Docs: https://fastapi.tiangolo.com/
🐳 Docker Guide: https://docs.docker.com/get-started/
🔥 PyTorch: https://pytorch.org/

📖 Documentation Deep Dives¶

Environment Creation Guide: src/envs/README.md
OpenSpiel Integration: src/envs/openspiel_env/README.md
Example Scripts: examples/
RFC 001: Baseline API Specs

🎓 Community & Support¶

Supported by amazing organizations:

🔥 Meta PyTorch
🤗 Hugging Face
⚡ Unsloth AI
🌟 Reflection AI
🚀 And many more!

License: BSD 3-Clause (very permissive!)

Contributions: Always welcome! Check out the issues tab.

🌈 What's Next?¶

⭐ Star the repo to show support and stay updated
🔄 Try modifying the Catch game (make it harder? bigger grid?)
🎮 Explore other OpenSpiel games
🛠️ Build your own environment integration
💬 Share what you build with the community!

🎯 Part 1-2: The Fundamentals - ⚡ RL in 60 seconds - 🤔 Why existing solutions fall short - 💡 The OpenEnv solution	🏗️ Part 3-5: The Architecture - 🔧 How OpenEnv works - 🔍 Exploring real code - 🎮 OpenSpiel integration example
🎮 Part 6-8: Hands-On Demo - 🔌 Use existing OpenSpiel environment - 🤖 Test 4 different policies - 👀 Watch learning happen live	🔧 Part 9-10: Going Further - 🎮 Switch to other OpenSpiel games - ✨ Build your own integration - 🌐 Deploy to production

OpenEnv: Production RL Made Simple¶

Why OpenEnv?¶

📋 What You'll Learn¶

📑 Table of Contents¶

Foundation¶

Architecture¶

Hands-On Demo¶

Advanced¶

Wrap Up¶

Part 1: RL in 60 Seconds ⏱️¶

Part 2: The Problem with Traditional RL 😤¶

🤔 Why Can't We Just Use OpenAI Gym?¶

💡 The OpenEnv Philosophy¶

The Architecture¶

Part 3: Setup 🛠️¶

Part 4: The OpenEnv Pattern 🏗️¶

Every OpenEnv Environment Has 3 Components:¶

Part 5: Example Integration - OpenSpiel 🎮¶

What is OpenSpiel?¶

OpenEnv's Integration¶

Type-Safe Models¶

How the Client Works¶

Part 6: Using Real OpenSpiel 🎮¶

The Game: Catch 🔴🏓¶

Part 7: Four Policies 🤖¶

Part 8: Policy Competition! 🏆¶

Part 9: Switching to Other Games 🎮¶

What We Just Used: Real OpenSpiel! 🎉¶

🎮 6 Games Available - Same Interface!¶

Try Another Game (Optional):¶

Part 10: Create Your Own Integration 🛠️¶

The 5-Step Pattern¶

Step 1: Define Types (models.py)¶

Step 2: Implement Environment (server/environment.py)¶

Step 3: Create Client (client.py)¶

Step 4: Create Server (server/app.py)¶

Step 5: Dockerize (server/Dockerfile)¶

🎓 Examples to Study¶

🎓 Summary: Your Journey¶

What You Learned¶

OpenEnv vs Traditional RL¶

📚 Resources¶

🔗 Essential Links¶

📖 Documentation Deep Dives¶

🎓 Community & Support¶

🌈 What's Next?¶

Step 1: Define Types (`models.py`)¶

Step 2: Implement Environment (`server/environment.py`)¶

Step 3: Create Client (`client.py`)¶

Step 4: Create Server (`server/app.py`)¶

Step 5: Dockerize (`server/Dockerfile`)¶