If you’re venturing into the world of reinforcement learning and want to explore the capabilities of Tensorforce, you’ve found the right place! This guide walks you through how to install, set up, and use Tensorforce effectively, as well as provides troubleshooting tips to assist you along your journey.

What is Tensorforce?

Tensorforce is an open-source deep reinforcement learning framework designed for both research and practical applications. Built on top of Google’s acclaimed TensorFlow framework, it leverages Python 3 to offer users a highly modular and user-friendly experience.

Installation

To get your Tensorforce journey underway, follow these simple installation steps.

• To install the stable version from PyPI, use:

  pip3 install tensorforce

• If you wish to always use the latest version, install from GitHub using:

  git clone https://github.com/tensorforce/tensorforce.git
  pip3 install -e tensorforce

Note for M1 Mac Users:

Currently, Tensorflow cannot be installed directly on M1 Macs. Please check the M1 Macs section in the documentation for a workaround.

Quickstart Example Code

To help you visualize how to utilize Tensorforce, let’s create an analogy. Imagine you’re setting up a mini soccer game. The environment is your soccer field, the agent is your player, and the actions your player can take (like kicking the ball, passing, or shooting) are the moves you expect from Tensorforce in your code. Here is how you would set everything up:

from tensorforce import Agent, Environment

# Pre-defined or custom environment
environment = Environment.create(
    environment='gym', level='CartPole', max_episode_timesteps=500)

# Instantiate a Tensorforce agent
agent = Agent.create(
    agent='tensorforce',
    environment=environment,
    memory=10000,
    update=dict(unit='timesteps', batch_size=64),
    optimizer=dict(type='adam', learning_rate=3e-4),
    policy=dict(network='auto'),
    objective='policy_gradient',
    reward_estimation=dict(horizon=20))

# Train for 300 episodes
for _ in range(300):
    # Initialize episode
    states = environment.reset()
    terminal = False
    while not terminal:
        # Episode timestep
        actions = agent.act(states=states)
        states, terminal, reward = environment.execute(actions=actions)
        agent.observe(terminal=terminal, reward=reward)

agent.close()
environment.close()

In this setup, you create an environment (the soccer field) and instantiate an agent (your player). You then train this agent over multiple episodes, akin to practicing on the field to improve skills and decision-making.

Command Line Usage

Tensorforce can be executed directly through the command line. To try out the well-known Proximal Policy Optimization (PPO) algorithm with the OpenAI Gym CartPole environment, use the command:

python3 run.py --agent benchmarks/configs/ppo.json --environment gym --level CartPole-v1 --episodes 100

This command runs the PPO implementation against the CartPole environment for a specified number of episodes.

Features

Tensorforce offers a plethora of features that make it versatile and powerful. Some of its highlights include:

• Support for various network layers (convolutional, RNNs, etc.)
• Policy distributions for different action types (Bernoulli, Gaussian, etc.)
• Multiple optimization algorithms directly from TensorFlow
• Configurability for reward estimation and training objectives
• Environment adapter support for popular environments such as Gym and CARLA

Troubleshooting

If you encounter any difficulties while working with Tensorforce, try the following troubleshooting tips:

• Ensure that all dependencies are properly installed, especially TensorFlow for optimal performance.
• Refer to the documentation for specific installation issues.
• For environment-specific problems, check the environment adapter’s documentation.
• Join the conversation on the Gitter channel to seek help from the community.

For more insights, updates, or to collaborate on AI development projects, stay connected with fxis.ai.

Conclusion

By following this guide, you will have a solid foundation in using Tensorforce to tackle reinforcement learning challenges. At fxis.ai, we believe that such advancements are crucial for the future of AI, as they enable more comprehensive and effective solutions. Our team is continually exploring new methodologies to push the envelope in artificial intelligence, ensuring that our clients benefit from the latest technological innovations.