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

  1. Environment – the simulated world (e.g., CartPole) that returns an observation, reward and done flag each step[24].
  2. Agent / Policy – the model that chooses actions to maximise cumulative reward.
  3. Replay Buffer – stores experience tuples for stable learning.
  4. 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:

ComponentReplace WithReference
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-keras and dm-reverb versions 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

  1. Experiment with other agents like PPO or C51 to compare performance[34].
  2. Create a custom environment—for game devs, wrap your Unity or Godot game state into a Gym-style API.
  3. 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.