The Mountain Car problem is a classic reinforcement learning problem used to evaluate the ability of an agent to learn to navigate a 1-dimensional environment. In this article, we will discuss the implementation of the Mountain Car problem.

Problem Description

The Mountain Car problem is a 1-dimensional environment where the agent starts at the bottom of a mountain and must learn to navigate to the top of the mountain by moving left or right. The agent receives a reward of -1 for every time step it takes, and a reward of +20 when it reaches the top of the mountain.

Implementation Steps

  1. Initialize the Environment: Set up the environment with the initial state, action space, and reward function.
  2. Choose an Algorithm: Select a reinforcement learning algorithm such as Q-learning or SARSA.
  3. Train the Agent: Use the selected algorithm to train the agent to learn the optimal policy.
  4. Evaluate the Agent: Test the agent's performance by running it in the environment and observing its behavior.

Code Example

import gym
import numpy as np

# Initialize the environment
env = gym.make("MountainCar-v0")

# Define the Q-table
Q_table = np.zeros([env.observation_space.n, env.action_space.n])

# Define the learning parameters
alpha = 0.1  # Learning rate
gamma = 0.99  # Discount factor
epsilon = 0.1  # Exploration rate

# Train the agent
for episode in range(1000):
    state = env.reset()
    done = False

    while not done:
        # Choose an action
        if np.random.uniform(0, 1) < epsilon:
            action = env.action_space.sample()
        else:
            action = np.argmax(Q_table[state])

        # Take the action and observe the next state and reward
        next_state, reward, done, _ = env.step(action)

        # Update the Q-table
        Q_table[state, action] = Q_table[state, action] + alpha * (reward + gamma * np.max(Q_table[next_state]) - Q_table[state, action])

        state = next_state

# Evaluate the agent
state = env.reset()
done = False

while not done:
    action = np.argmax(Q_table[state])
    state, reward, done, _ = env.step(action)
    env.render()

env.close()

Further Reading

For more information on the Mountain Car problem and reinforcement learning, please refer to the following resources:

Mountain Car Environment