Train Your First Deep Q Learning-Based RL Agent: A Step-by-Step Guide | by smit kumbhani | May 2023

Reinforcement Learning (RL) is a fascinating field of artificial intelligence (AI) that allows machines to learn and make decisions by interacting with their environment. Learning an RL agent involves a trial-and-error process where the agent learns about its actions and the subsequent rewards or penalties it receives. In this blog, we’ll walk through the steps involved in training your first RL agent, as well as code snippets to illustrate the process.

The first step in training an RL agent is to define the environment in which it will operate. The environment can be a simulation or a real scenario. It provides the agent with observations and rewards that allow it to learn and make decisions. OpenAI Gym is a popular Python library that provides a wide range of pre-built environments. Let’s consider the classic CartPole environment for this example.

import gym
env = gym.make('CartPole-v1')

In RL, an agent interacts with the environment, taking actions based on its observations. It receives feedback in the form of rewards or punishments that are used to guide its learning process. The agent’s goal is to maximize cumulative rewards over time. To do this, the agent learns a policy—a map from observations to actions—that helps it make the best decisions.

Various RL algorithms are available, each with their own strengths and weaknesses. One of the popular algorithms is Q-Learning, which is suitable for discrete action spaces. Another commonly used algorithm is Deep Q-Networks (DQN), which uses deep neural networks to process complex environments. For this example, let’s use the DQN algorithm.

To build an RL agent using the DQN algorithm, we need to define a neural network as a function approximator. The network takes observations as input and outputs Q-values ​​for each possible action. We also need to implement recurrent memory to store and review experiences for training.

import torch
import torch.nn as nn
import torch.optim as optim
class DQN(nn.Module):
def __init__(self, input_dim, output_dim):
super(DQN, self).__init__()
self.fc1 = nn.Linear(input_dim, 64)
self.fc2 = nn.Linear(64, 64)
self.fc3 = nn.Linear(64, output_dim)
def forward(self, x):
x = torch.relu(self.fc1(x))
x = torch.relu(self.fc2(x))
x = self.fc3(x)
return x
# Create an instance of the DQN agent
input_dim = env.observation_space.shape[0]
output_dim = env.action_space.n
agent = DQN(input_dim, output_dim) 

Step 5: Prepare the RL agent

Now we can train the RL agent using the DQN algorithm. An agent interacts with the environment, observes the current state, chooses an action based on its policy, receives a reward, and updates its Q-values ​​accordingly. This process is repeated for a specified number of episodes or until the agent reaches a satisfactory level of performance.

optimizer = optim.Adam(agent.parameters(), lr=0.001)
def train_agent(agent, env, episodes):
for episode in range(episodes):
state = env.reset()
done = False
episode_reward = 0
while not done:
action = agent.select_action(state)
next_state, reward, done, _ = env.step(action)
agent.store_experience(state, action, reward, next_state, done)
agent

In this blog we explore the process of training your first RL agent. We started by defining the environment using OpenAI Gym, which provides a pre-built environment for RL tasks. We then discuss agent-environment interactions and the agent’s goal of maximizing cumulative rewards.

Next, we chose the DQN algorithm as our RL algorithm of choice, which combines deep neural networks with Q-learning to process complex environments. We built an RL agent using a neural network as a function approximator and implemented recurrent memory to store and test the experience for training.

Finally, we trained the RL agent by interacting with the environment, observing states, choosing actions based on its policies, receiving rewards, and updating its Q-values. This process was repeated for a specified number of episodes, allowing the agent to learn and improve its decision-making capabilities.

Reinforcement Learning opens up a world of possibilities for training intelligent agents that can learn and make decisions independently in dynamic environments. By following the steps outlined in this blog, you can begin your journey by training RL agents and exploring different algorithms, environments, and applications.

Remember, RL training requires experimentation, refinement, and patience. As you delve into RL, you can explore advanced techniques such as deep RL, policy gradients, and multi-agent systems. So keep learning, iterating, and pushing the limits of what your RL agents can achieve.

Happy training!

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