Vision

Vision

We bring reinforcement learning into the real world.

RL

TensorForce

Our production-grade open source reinforcement learning library.

Enterprise version

Enterprise version

Deploy, train and serve TensorForce models.

Vision

Reinforcement learning in the real world is hard. Let us make the software part easy for you. We build enterprise-grade deep reinforcement learning software and services. Our goal is to provide fully portable computation graphs for reinforcement learning in any industrial context, be it robotics, autonomous vehicles, manufacturing, or finance.

TensorForce

TensorForce is an open source reinforcement learning library on top of TensorFlow focused on providing clear APIs, readability and modularisation to deploy reinforcement learning solutions both in research and practice.

from tensorforce.agents import PPOAgent

# Create a Proximal Policy Optimization agent
agent = PPOAgent(
    states_spec=dict(type='float', shape=(10,)),
    actions_spec=dict(type='int', num_actions=10),
    network_spec=[
        dict(type='dense', size=64),
        dict(type='dense', size=64)
    ],
    batch_size=1000,
    step_optimizer=dict(
        type='adam',
        learning_rate=1e-4
    )
)

# Get new data from somewhere, e.g. a client to a web app
client = MyClient('http://127.0.0.1', 8080)

# Poll new state from client
state = client.get_state()

# Get prediction from agent, execute
action = agent.act(state)
reward = client.execute(action)

# Add experience, agent automatically updates model according to batch size
agent.observe(reward=reward, terminal=False)

Stay up to date

Register your interest for very (!) occasional updates on our progress: