How to interpret the observations of RAM environments in OpenAI gym?

openai gym observation
openai gym environments
openai gym custom environment
openai gym environment name
openai universe environments
openai gym continuous action space
openai gym environments tutorial
openai gym gridworld

In some OpenAI gym environments, there is a "ram" version. For example: Breakout-v0 and Breakout-ram-v0.

Using Breakout-ram-v0, each observation is an array of length 128.

Question: How can I transform an observation of Breakout-v0 (which is a 160 x 210 image) into the form of an observation of Breakout-ram-v0 (which is an array of length 128)?

My idea is to train a model on the Breakout-ram-v0 and display the trained model playing using the Breakout-v0 environment.


There's a couple ways of understanding the ram option.

Let's say you wanted to learn pong. If you train from the pixels, you'll likely use a convolutional net of several layers. interestingly, the final output of the convnet is a a 1D array of features. These you pass to a fully connected layer and maybe output the correct 'action' based on the features the convnet recognized in the image(es). Or you might use a reinforcement layer working on the 1D array of features.

Now let's say it occurs to you that pong is very simple, and could probably be represented in a 16x16 image instead of 160x160. straight downsampling doesn't give you enough detail, so you use openCV to extract the position of the ball and paddles, and create your mini version of 16x16 pong. with nice, crisp pixels. The computation needed is way less than your deep net to represent the essence of the game, and your new convnet is nice and small. Then you realize you don't even need your convnet any more. you can just do a fully connected layer to each of your 16x16 pixels.

So, think of what you have. Now you have 2 different ways of getting a simple representation of the game, to train your fully-connected layer on. (or RL algo)

  1. your deep convnet goes through several layers and outputs a 1D array, say of 256 features in the final layer. you pass that to the fully connected layer.
  2. your manual feature extraction extracts the blobs (pattles/ball) with OpenCV, to make a 16x16 pong. by passing that to your fully connected layer, it's really just a set of 16x16=256 'extracted features'.

So the pattern is that you find a simple way to 'represent' the state of the game, then pass that to your fully connected layers.

Enter option 3. The RAM of the game may just be a 256 byte array. But you know this contains the 'state' of the game, so it's like your 16x16 version of pong. it's most likely a 'better' representation than your 16x16 because it probably has info about the direction of the ball etc.

So now you have 3 different ways to simplify the state of the game, in order to train your fully connected layer, or your reinforcment algorithm.

So, what OpenAI has done by giving you the RAM is helping you avoid the task of learning a 'representation' of the game, and that let's you move directly to learning a 'policy' or what to do based on the state of the game.

OpenAI may provide a way to 'see' the visual output on the ram version. If they don't, you could ask them to make that available. But that's the best you will get. They are not going to reverse engineer the code to 'render' the RAM, nor are they going to reverse engineer the code to 'generate' 'RAM' based on pixels, which is not actually possible, since pixels are only part of the state of the game.

They simply provide the ram if it's easily available to them, so that you can try algorithms that learn what to do assuming there is something giving them a good state representation.

There is no (easy) way to do what you asked, as in translate pixels to RAM, but most likely there is a way to ask the Atari system to give you both the ram, and the pixels, so you can work on ram but show pixels.

Breakout-ram-v0, Maximize your score in the Atari 2600 game Breakout. In this environment, the observation is the RAM of the Atari machine, consisting of (only!) 128 bytes. Alien-ram-v0. Maximize score in the game Alien, with RAM as input


While the above answer is correct in terms of reinforcement learning strategy and the inability to directly convert ram to image or vice versa, to grab the ram state from an image environment, you can use

# this is an image based environment
env = gym.make('Breakout-v0')
env.reset()

# put in the 0 action 
observation_image, reward, done, info = env.step(0)

# get the ram observation with the code below
observation_ram = env.unwrapped._get_ram()

Documentation, Getting Started with Gym. Installation. Building from Source. Environments; Observations; Spaces. Available Environments. The registry. Background: Why Gym? Gym comes with a diverse suite of environments that range from easy to difficult and involve many different kinds of data. View the full list of environments to get the birds-eye view. Classic control and toy text : complete small-scale tasks, mostly from the RL literature.


My idea is to train a model on the Breakout-ram-v0 and display the trained model playing using the Breakout-v0 environment.

Similar to erosten's answer: If your environment is

env = gym.make('Breakout-ram-v0')
env.reset()

and you want pixels, you're looking for

pixels = env.unwrapped._get_image()

ElevatorAction-ram-v0, Maximize your score in the Atari 2600 game ElevatorAction. In this environment, the observation is the RAM of the Atari machine, consisting of (only!) 128 bytes. Open source interface to reinforcement learning tasks. The gym library provides an easy-to-use suite of reinforcement learning tasks.. import gym env = gym.make("CartPole-v1") observation = env.reset() for _ in range(1000): env.render() action = env.action_space.sample() # your agent here (this takes random actions) observation, reward, done, info = env.step(action) if done: observation = env


You can simply use ram environment of Atari for training and call the wrappers object to automatically save the trained videos.

import gym
from gym import wrappers
env = gym.make('SpaceInvaders-ram-v0')
env = wrappers.Monitor(env, "/path/to/folder/", force=True)
class(Policy):
    "Do your thing"


train_function() #call your train function

Tutankham-ram-v0, Maximize your score in the Atari 2600 game Tutankham. In this environment, the observation is the RAM of the Atari machine, consisting of (only!) 128 bytes. An environment is a problem with a minimal interface that an agent can interact with. The environments in the OpenAI Gym are designed in order to allow objective testing and bench-marking of an agents abilities. Adding New Environments. Write your environment in an existing collection or a new collection. All collections are subfolders of `/gym


Encoding OpenAI Gym's Input - Engineering, The environment I choose for my agent to learn and play is OpenAI There are two types of input(observation) I can choose from: Ram and Game Image After reading the doco it seems my understanding of OpenAI gym is� Maximize your score in the Atari 2600 game AirRaid. In this environment, the observation is the RAM of the Atari machine, consisting of (only!) 128 bytes. Each action is repeatedly performed for a duration of \(k\) frames, where \(k\) is uniformly sampled from \(\{2, 3, 4\}\).


Table of environments � openai/gym Wiki, The OpenAI Gym provides 59 Atari 2600 games as environments. You can read more about the choice of this metric in the Rainbow paper. Also, there are RAM environments such as Pong-ram-v0 , where the observation is the RAM of the� Maximize your score in the Atari 2600 game JourneyEscape. In this environment, the observation is the RAM of the Atari machine, consisting of (only!) 128 bytes. Each action is repeatedly performed for a duration of \(k\) frames, where \(k\) is uniformly sampled from \(\{2, 3, 4\}\).


Atari Environments, For Breakout, we employed OpenAI Gym's Atari game environment 1) Environment states: 1x128 RAM state of Breakout. 2) Action 4) Reward Rules: The score of the game lated observation sequences, which is especially important in. Welcome to the OpenAI Gym wiki! Feel free to jump in and help document how the OpenAI gym works, summarize findings to date, preserve important information from gym's Gitter chat rooms, surface great ideas from the discussions of issues, etc.