Q-Learning using GenRL

What is Q-Learning?

Q-Learning is one of the stepping stones for many reinforcement learning algorithms like DQN. AlphaGO is also one of the famous examples that use Q-Learning at the heart.

Essentially, a RL agent take an action on the environment and then collect rewards and update its policy, and over time gets better at collecting higher rewards.

In Q-Learning, we generally maintain a “Q-table” of Q-values by mapping them to a (state, action) pair.

A natural question is, What are these Q-values ? It is nothing but the “Quality” of an action taken from a particular state. The more the Q-value the more chances of getting a better reward.

Q-Table is often initialized with random values/with zeros and as the agent collects rewards via performing actions on the environment we update this Q-Table at the \(i\) th step using the following formulation -

\[Q_{i}(s, a) = (1- \alpha)Q_{i-1}(s, a) + \alpha * (reward + \gamma * max_{a'} Q_{i-1}(s', a'))\]

Here \(\alpha\) is the learning rate in ML terms, \(\gamma\) is the discount factor for the rewards and \(s'\) is the state reached after taking action \(a\) from state \(s\).

FrozenLake-v0 environment

So to demonstrate how easy it is to train a Q-Learning approach in GenRL, we are taking a very simple gym environment.

Description of the environment (from the documentation) -

“The agent controls the movement of a character in a grid world. Some tiles of the grid are walkable, and others lead to the agent falling into the water. Additionally, the movement direction of the agent is uncertain and only partially depends on the chosen direction. The agent is rewarded for finding a walkable path to a goal tile.

Winter is here. You and your friends were tossing around a frisbee at the park when you made a wild throw that left the frisbee out in the middle of the lake. The water is mostly frozen, but there are a few holes where the ice has melted. If you step into one of those holes, you’ll fall into the freezing water. At this time, there’s an international frisbee shortage, so it’s absolutely imperative that you navigate across the lake and retrieve the disc. However, the ice is slippery, so you won’t always move in the direction you intend.

The surface is described using a grid like the following:

SFFF       (S: starting point, safe)
FHFH       (F: frozen surface, safe)
FFFH       (H: hole, fall to your doom)
HFFG       (G: goal, where the frisbee is located)

The episode ends when you reach the goal or fall in a hole. You receive a reward of 1 if you reach the goal, and zero otherwise.”


Let’s import all the usefull stuff first.

import gym
from genrl import QLearning                             # for the agent
from genrl.classical.common import Trainer              # for training the agent

Now that we have imported all the necessary stuff let’s go ahead and define the environment, the agent and an object for the Trainer class.

env = gym.make("FrozenLake-v0")
agent = QLearning(env, gamma=0.6, lr=0.1, epsilon=0.1)
trainer = Trainer(

Great so far so good! Now moving towards the training process it is just calling the train method in the trainer class.


That’s it! You have successfully trained a Q-Learning agent. You can now go ahead and play with your own environments using GenRL!