dmc2gym

Introduction to dmc2gym

The dmc2gym project is a straightforward yet powerful wrapper designed to bridge the gap between the DeepMind Control Suite and the widely-used OpenAI Gym interface. This integration allows users to leverage the impressive control environments provided by DeepMind while maintaining the familiar and standardized interface of OpenAI Gym, making it easier to develop and test reinforcement learning algorithms.

Core Features

1. Random Seed Initialization: One of the key features offered by the dmc2gym wrapper is the reliable initialization of random seeds. This is particularly important for researchers and developers who need deterministic behavior in their experiments to ensure reproducibility and consistency.

2. Image-Based Observations: By setting the parameter from_pixels=True, users can convert proprioceptive observations into image-based ones. This transformation is crucial for certain applications, such as those involving visual perception, where observations need to resemble images. Additionally, users have the flexibility to define the dimensions of these images by adjusting the height and width attributes.

3. Action Space Normalization: To simplify the handling of action spaces, the dmc2gym package normalizes each action's coordinate to fall within the [-1, 1] range. This standardization is beneficial because it creates a common scale across different environments, improving learning stability and algorithm performance.

4. Action Repeat via Frame Skipping: The frame_skip parameter allows users to enable action repeating, which effectively means that one action can be executed for several consecutive frames. This technique can speed up training by reducing the frequency of environment interactions required, which can be particularly handy in real-time applications.

Installation

Getting started with dmc2gym is as easy as executing a single command in the terminal. This command fetches the package directly from GitHub:

pip install git+git://github.com/denisyarats/dmc2gym.git

Basic Usage

Utilizing the dmc2gym wrapper is intuitive. Below is a simple example to illustrate its usage:

import dmc2gym

# Create an environment using a specific domain and task
env = dmc2gym.make(domain_name='point_mass', task_name='easy', seed=1)

# Run a simulation loop
done = False
obs = env.reset()
while not done:
  action = env.action_space.sample()  # Sample random actions
  obs, reward, done, info = env.step(action)  # Apply action in the environment

In this example, the point_mass domain with the easy task is instantiated. The loop runs until the task is complete (done becomes True), randomly sampling actions and applying them to the environment.

Overall, dmc2gym stands out as a versatile tool for those looking to combine DeepMind's extensive control environments with the simplicity and reliability of the OpenAI Gym interface. Its ease of use and robust features make it a valuable addition to any reinforcement learning toolbox.