Build Your First AI Agent from Scratch with TensorFlow & TF-Agents
Creating an AI agent from scratch may sound intimidating, but modern libraries like TensorFlow Agents (TF-Agents) make reinforcement-learning (RL) accessible—even if you are new to machine learning[35][12]. This beginner-friendly guide walks through installing the tools, writing a minimal Deep Q-Network (DQN) agent, training it on a classic problem, and extending the template for your own apps.
1 · Prerequisites
- Python 3.9 or later
- pip or Conda environment
- Basic Python syntax knowledge (no prior ML required)
2 · Install TensorFlow & TF-Agents
# CPU-only setup
pip install tensorflow==2.16.0 tf-keras # core ML engine
pip install tf-agents[reverb] # RL components
The optional [reverb] extra pulls Google’s Reverb replay buffer used in most TF-Agents examples[35].
3 · Understand the RL Pipeline Quickly
- Environment – the simulated world (e.g., CartPole) that returns an observation, reward and done flag each step[24].
- Agent / Policy – the model that chooses actions to maximise cumulative reward.
- Replay Buffer – stores experience tuples for stable learning.
- Trainer Loop – collects experience, updates the network, and evaluates progress.
4 · Hands-On: Build a Minimal DQN Agent
4.1 · Import Libraries
import tensorflow as tf
from tf_agents.environments import suite_gym, tf_py_environment
from tf_agents.networks import q_network
from tf_agents.agents.dqn import dqn_agent
from tf_agents.replay_buffers import tf_uniform_replay_buffer
from tf_agents.utils import common
4.2 · Load the CartPole Environment
train_env = tf_py_environment.TFPyEnvironment(
suite_gym.load('CartPole-v1'))
eval_env = tf_py_environment.TFPyEnvironment(
suite_gym.load('CartPole-v1'))
4.3 · Define the Q-Network
fc_layers = (128, 128) # two hidden layers
q_net = q_network.QNetwork(
train_env.observation_spec(),
train_env.action_spec(),
fc_layer_params=fc_layers)
4.4 · Configure the DQN Agent
optimizer = tf.keras.optimizers.Adam(1e-3)
global_step = tf.Variable(0, dtype=tf.int64)
agent = dqn_agent.DqnAgent(
train_env.time_step_spec(),
train_env.action_spec(),
q_network=q_net,
optimizer=optimizer,
td_errors_loss_fn=common.element_wise_squared_loss,
train_step_counter=global_step)
agent.initialize()
The DqnAgent wraps the network, exploration strategy, and training logic for you[44].
4.5 · Build a Replay Buffer
replay_buffer = tf_uniform_replay_buffer.TFUniformReplayBuffer(
data_spec=agent.collect_data_spec,
batch_size=train_env.batch_size,
max_length=100000)
4.6 · Training Loop (Simplified)
num_iterations = 15000
collect_driver = ... # see full TF-Agents tutorial for details
for _ in range(num_iterations):
collect_driver.run() # gather 1 step
experience = replay_buffer.gather_all()
train_loss = agent.train(experience) # gradient update
replay_buffer.clear()
After ~10 k iterations the agent should balance the pole for 500 steps consistently, achieving a reward ≥475 on evaluation episodes[44][9].
5 · Extend to Your Own Problem
The same template scales to new tasks by swapping three pieces:
| Component | Replace With | Reference |
|---|---|---|
| Environment | Your custom py_environment.PyEnvironment subclass (e.g., a game or robotics simulator) |
[20] |
| Network Arch. | Convolutional or recurrent layers for images or time-series | [18] |
| Agent Type | PPO, SAC, C51 or REINFORCE for continuous or stochastic tasks | [34][18] |
6 · Save & Deploy the Trained Policy
policy_dir = 'saved_policy'
tf_policy_saver = tf.compat.v2.saved_model.SavePolicy(agent.policy)
tf_policy_saver.save(policy_dir)
The SavedModel can be served with TensorFlow Serving or converted to TensorFlow Lite for mobile deployments[36].
7 · Troubleshooting Tips
- Installation errors: ensure
tf-agents,tf-kerasanddm-reverbversions match the TensorFlow build[35][33]. - Training diverges: start with a smaller learning rate (1e-4) or increase replay buffer capacity[44].
- Slow GPU usage: verify TensorFlow detects CUDA; otherwise the code runs on CPU.
8 · Next Steps
- Experiment with other agents like PPO or C51 to compare performance[34].
- Create a custom environment—for game devs, wrap your Unity or Godot game state into a Gym-style API.
- Deploy the trained model inside a Flutter game using TFLite’s FFI bindings.
Congratulations! You now have a working blueprint for building AI agents with TensorFlow from absolute scratch. Use it as a springboard for smarter apps, autonomous game NPCs, or real-world robotics projects.