Unity ML Agents

Environment Basics

This notebook contains a walkthrough of the basic functions of the Python API for Unity ML Agents. For instructions on building a Unity environment, see here.

1. Load dependencies



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import matplotlib.pyplot as plt
import numpy as np

from unityagents import UnityEnvironment

%matplotlib inline

2. Set environment parameters

Be sure to set env_name to the name of the Unity environment file you want to launch.



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env_name = "3DBall" # Name of the Unity environment binary to launch
train_mode = True # Whether to run the environment in training or inference mode

3. Start the environment

UnityEnvironment launches and begins communication with the environment when instantiated.

Environments contain brains which are responsible for deciding the actions of their associated agents. Here we check for the first brain available, and set it as the default brain we will be controlling from Python.



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env = UnityEnvironment(file_name=env_name)

# Examine environment parameters
print(str(env))

# Set the default brain to work with
default_brain = env.brain_names[0]
brain = env.brains[default_brain]

4. Examine the observation and state spaces

We can reset the environment to be provided with an initial set of observations and states for all the agents within the environment. In ML-Agents, states refer to a vector of variables corresponding to relevant aspects of the environment for an agent. Likewise, observations refer to a set of relevant pixel-wise visuals for an agent.



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# Reset the environment
env_info = env.reset(train_mode=train_mode)[default_brain]

# Examine the state space for the default brain
print("Agent state looks like: \n{}".format(env_info.states[0]))

# Examine the observation space for the default brain
for observation in env_info.observations:
    print("Agent observations look like:")
    if observation.shape[3] == 3:
        plt.imshow(observation[0,:,:,:])
    else:
        plt.imshow(observation[0,:,:,0])

5. Take random actions in the environment

Once we restart an environment, we can step the environment forward and provide actions to all of the agents within the environment. Here we simply choose random actions based on the action_space_type of the default brain.



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for episode in range(10):
    env_info = env.reset(train_mode=train_mode)[default_brain]
    done = False
    episode_rewards = 0
    while not done:
        if brain.action_space_type == 'continuous':
            env_info = env.step(np.random.randn(len(env_info.agents), 
                                                brain.action_space_size))[default_brain]
        else:
            env_info = env.step(np.random.randint(0, brain.action_space_size, 
                                                  size=(len(env_info.agents))))[default_brain]
        episode_rewards += env_info.rewards[0]
        done = env_info.local_done[0]
    print("Total reward this episode: {}".format(episode_rewards))

6. Close the environment when finished

When we are finished using an environment, we can close it with the function below.



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env.close()