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gym-moving-dot

A simple environment for OpenAI gym consisting of a white dot moving around in a black square, designed as a simple test environment for reinforcement learning experiments.

Observations are given as 210 x 160 pixel image with 3 channels for red, green and blue; the same size as Atari environments. The white dot has pixel values (255, 255, 255), while the black square has pixel values (0, 0, 0).

Possible actions are:

• Discrete action_space
  • 0: do nothing
  • 1: move down
  • 2: move right
  • 3: move up
  • 4: move left
• Continuous action_space
  • Action: 1 x 2 vector for [move_on_x, move_on_y]
  • Range: -1 <= move_on_x <= 1, -1 <= move_on_y <= 1
  • Rules

    # Rules on executing an action
    new_x = original_x_pos + 1 if move_on_x >= threshold else original_x_pos - 1
    new_y = original_y_pos + 1 if move_on_y >= threshold else original_y_pos - 1
    

Rewards are given based on how far the dot is from the centre.

• If the dot moves closer to the centre, it receives reward +1.
• If the dot moves further away from the centre, it receives reward -1.
• If the dot sames the same distance from the centre, it receives reward 0.

The episode terminates after a given number of steps have been taken (by default 1,000). If env.random_start is set to True (the default), the dot starts in a different position at the start of each episode. Otherwise, the dot starts at the top left corner.

Training with actor-critic (A2C from OpenAI's baselines with one worker) takes about five minutes to achieve good reward. After about 20 minutes of training, expect your graphs to look something like:

Installation

pip install --user git+https://github.com/mrahtz/gym-moving-dot

Dependencies

pip install -r requirements.txt

Usage

import gym
import gym_moving_dot

ENVS = ["MovingDotDiscrete-v0",
        "MovingDotDiscreteNoFrameskip-v0",
        "MovingDotContinuous-v0",
        "MovingDotContinuousNoFrameskip-v0"]

for env_name in ENVS:
    print("=== Test: {} ===".format(env_name))

    env = gym.make(env_name)
    env.random_start = False

    env.reset()

    for i in range(3):
        a = env.action_space.sample()
        o, r, d, info = env.step(a)
        print("Obs shape: {}, Action: {}, Reward: {}, Done flag: {}, Info: {}".format(o.shape, a, r, d, info))

    env.close()
    del env

Update

• 1/11/2019:
  • update to be compatible with the latest gym package
  • add the continuous action_space version
• 16/12/2019:
  • separate the existing classes into the parent and subclasses