Developing New Machine Learning Algorithms using OpenAI Gym

ByJordan Brown

Introduction

Machine learning, and reinforcement learning in particular, has made tremendous progress in recent years. We‘ve seen AI achieve superhuman performance on challenging games like Go, Dota, and Starcraft. But developing and testing new reinforcement learning algorithms remains difficult. Fortunately, OpenAI Gym provides a suite of environments and tools to make the process easier. In this article, we‘ll take an in-depth look at OpenAI Gym and how you can use it to accelerate your machine learning research and development.

What is OpenAI Gym?

OpenAI Gym is an open-source library that provides a wide range of simulated environments for testing and developing reinforcement learning algorithms. It was originally created by OpenAI, an artificial intelligence research company (now Anthropic), to help standardize the setup for training and benchmarking RL agents.

The environments in OpenAI Gym range from simple text-based games to Atari video games, board games, continuous control tasks with simulated robots, and more. Each environment follows the same interface, allowing developers to write code that can easily switch between different tasks.

Having this standardized set of challenging environments is hugely beneficial for the research community. It allows fair comparisons between algorithms, makes reproducing results easier, and lets developers focus more on their learning algorithms rather than spending time building custom environments from scratch.

Getting Started with OpenAI Gym

Installing OpenAI Gym on your machine is straightforward using pip:

pip install gym

Some environments (like the Atari games) have additional dependencies, but the core library installs quickly and easily.

Once you have Gym installed, you can create an instance of an environment with the gym.make() function, passing the ID of the environment you want:

import gym
env = gym.make(‘CartPole-v1‘)

This creates an instance of the classic CartPole environment, a simple setup where the goal is to balance an upright pole on a cart that can move left or right. Every environment has a few key methods you‘ll use to interact with it:

  • reset() resets the environment to its initial state and returns the first observation
  • step(action) advances the environment one timestep based on the given action and returns an observation, reward, done flag, and info dict
  • render() generates a rendered image of the environment in its current state

A basic loop for using an environment looks like this:

obs = env.reset()
done = False
while not done:
    action = my_policy(obs)
    obs, reward, done, info = env.step(action)

my_policy here is a placeholder for whatever policy or learning algorithm you‘re testing – it takes the current observation as input and returns an action to take.

Exploring OpenAI Gym Environments

One of the great things about OpenAI Gym is the wide variety of environments it provides off the shelf for training and testing. There are a few major categories worth knowing about:

Classic Control environments are simple setups like CartPole, MountainCar, or Acrobot. They have small observation and action spaces, making them great for sanity checks and smaller-scale testing.

Toy Text environments are simple grid-world style text games like FrozenLake and Taxi. Again, great for testing algorithms on small state and action spaces.

Atari environments let you train RL agents to play various classic Atari games, using the game screen pixels as input. This is a challenging domain that was instrumental in the development of Deep Q-Networks and other groundbreaking RL advancements.

Box2D and MuJoCo provide continuous control tasks, often involving simulated robots. These let you work on algorithms that output continuous actions (rather than discrete choices) to control walking, running, hopping, and other behaviors.

Beyond these builtins, you can also define your own custom environments that adhere to the OpenAI Gym interface. This lets you test algorithms on new tasks or variations tailored to your particular needs.

Developing RL Algorithms in OpenAI Gym

So how do you actually use OpenAI Gym when developing a new RL algorithm? Let‘s walk through a basic example.

We‘ll implement a simple policy gradient method and train it on the CartPole-v1 environment. Policy gradient methods learn a parameterized policy that can select actions given states. They work by computing an estimate of the policy gradient and performing gradient ascent to find policy parameters that maximize expected reward.

Here‘s a bare-bones implementation using PyTorch, but the principles apply with any deep learning library:

import torch
import torch.nn as nn
import torch.optim as optim
from torch.distributions.categorical import Categorical

class Policy(nn.Module):
def init(self):
super().init()
self.fc1 = nn.Linear(4, 128)
self.fc2 = nn.Linear(128, 2)


def forward(self, x):
    x = torch.relu(self.fc1(x))
    x = self.fc2(x)
    return x


env = gym.make(‘CartPole-v1‘)

policy = Policy()

optimizer = optim.Adam(policy.parameters(), lr=1e-2)


def run_episode(env, policy):

obs = env.reset()

rewards = []
logprobs = []

done = False
while not done:
    action_logits = policy(torch.FloatTensor(obs))
    dist = Categorical(logits=action_logits)
    action = dist.sample()
    logprob = dist.log_prob(action)
    obs, reward, done, info = env.step(action.item())

    rewards.append(reward)
    logprobs.append(logprob)

return rewards, logprobs


episodes = 500

gamma = 0.98


for ep in range(episodes):

rewards, logprobs = run_episode(env, policy)

discounted_rewards = []

for t in range(len(rewards)):
    future_reward = 0 
    for k in range(t+1, len(rewards)):
        future_reward += (gamma**(k-t-1)) * rewards[k]
    discounted_rewards.append(future_reward)

discounted_rewards = torch.FloatTensor(discounted_rewards)
discounted_rewards = (discounted_rewards - discounted_rewards.mean()) / (discounted_rewards.std() + 1e-7)

policy_gradient = []
for dR, logprob in zip(discounted_rewards, logprobs):
    policy_gradient.append(-logprob * dR)

optimizer.zero_grad()
policy_loss = torch.stack(policy_gradient).sum()
policy_loss.backward()
optimizer.step()


This is of course a simplification, but it illustrates the key pieces: creating an environment, defining a policy, running the policy in the environment, and using the results to update the policy parameters via gradient ascent.


You can swap in different algorithms, add logging to track performance over time, run multiple random seeds for statistical confidence, and so on. The key is that OpenAI Gym makes it easy to test your algorithm on a variety of standardized tasks.


Tips and Best Practices


Here are a few tips to keep in mind as you use OpenAI Gym to develop new RL algorithms:


    

  • 

    Log everything and visualize performance metrics over time. It‘s hard to tell if your algorithm is working just by watching it. Plot episode rewards, track losses, and use rolling averages to smooth out noise.
    

    • 

  • 

    Run multiple seeds to assess variance. RL algorithms can be unstable and vary significantly between different random initializations. To be rigorous, you should run several independent training runs and aggregate the results.
    

    • 

  • 

    Start simple and scale up gradually. It‘s tempting to jump right into complex Atari environments, but you‘re more likely to catch bugs and make faster progress if you start with the simplest environment that exhibits the core challenge you‘re interested in. Simple doesn‘t mean easy!
    

    • 

  • 

    Double-check your reward functions. The learning process is highly sensitive to the choice of reward function. A mistake here can lead to unintended behaviors. Where possible, verify your rewards against the published literature.
    

    • 

  • 

    Take advantage of wrappers and libraries. You don‘t have to build everything from scratch. OpenAI Baselines includes reference implementations of several SOTA algorithms. Keras-RL and Stable Baselines provide higher-level abstractions. Ray RLlib helps with distributed training.
    

    • 



Applications and Future Potential


The development of new, more sample-efficient and generalizable RL algorithms is an active area of research with major implications for the future of artificial intelligence.


Imagine training a robot to navigate new environments, perform complex manipulation tasks, or adaptively interact with human users. Or AI systems that can flexibly learn to optimize power systems, computer networks, transportation infrastructure, and more. Tools like OpenAI Gym bring us closer to making these types of applications practical.


Recent projects like GPT-3 and DALL-E hint at the potential for RL-based systems that learn open-ended behaviors and world models in much the same way these language models learn patterns from text. While current algorithms still struggle with the kind of long-range credit assignment and abstraction needed for human-level abilities, progress is steady and the future is bright.


Conclusion


OpenAI Gym is a powerful tool for anyone looking to develop new machine learning algorithms, especially in the field of reinforcement learning. It provides a standardized interface to a variety of challenging environments, making it easier to implement, test, and compare different approaches.


Whether you‘re debugging a new algorithm, reproducing results from a paper, or exploring a completely novel approach, OpenAI Gym can help provide structure to your research and development process. By combining Gym with modern deep learning frameworks and following rigorous experimental practices, you‘ll be well-equipped to make your own contributions to this exciting field.


So what are you waiting for? Install OpenAI Gym and start training some agents! And if you come up with something particularly interesting, consider open-sourcing your code so the rest of the community can learn from your work. Happy researching!


      
          
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