Implementing Reinforcement Learning (RL) for Robotics using Open Source Software

September 20, 2022

When it comes to robotics, Reinforcement Learning (RL) is one of the most popular methods for enabling an agent to interact with its environment and learn from it. RL is a type of machine learning that allows an agent to learn from trial and error by observing the feedback it receives from its actions. In this blog post, we will explore how to implement RL for robotics using open source software.

Introduction

RL is an effective approach for enabling robots to learn by interacting with their environment. The idea is to create an agent that can take actions in its environment and receive feedback based on those actions. The agent then uses this feedback to adjust its behavior so that it can achieve a desired goal. The feedback can be positive or negative, depending on whether the agent’s action moves it closer to or farther away from the goal.

OpenAI Gym

OpenAI Gym is an open source toolkit for developing and comparing reinforcement learning algorithms. It provides a set of environments for RL experiments, as well as a set of tools for developing and testing agents. Some of the popular environments included in OpenAI Gym are Atari, CartPole, and MountainCar.

To install OpenAI Gym, run the following command in your terminal:

pip install gym

Once installed, you can import the toolkit and create an instance of an environment as follows:

import gym
env = gym.make('CartPole-v0')

Here, we are creating an instance of the CartPole environment, which consists of a cart that can move back and forth on a track, and a pole attached to the cart. The goal is to keep the pole balanced by moving the cart left or right.

Q-learning

Q-learning is a popular reinforcement learning technique that is used to find the optimal action policy for an agent in a given environment. The Q-learning algorithm involves building a table (known as the Q-table) that contains the expected reward for each action at each state. The Q-table is iteratively updated based on the feedback the agent receives from its actions.

To implement Q-learning in OpenAI Gym, we first need to create a Q-table. We can do this as follows:

import numpy as np
q_table = np.zeros([env.observation_space.n, env.action_space.n])

Here, we are creating a Q-table with dimensions equal to the number of possible states and actions in the environment. We initialize all the values in the table to zero.

Next, we can create a function to run the Q-learning algorithm:

def q_learning(env, alpha, gamma, epsilon, episodes):
    for episode in range(episodes):
        state = env.reset()
        done = False
        while not done:
            if np.random.uniform() < epsilon:
                action = env.action_space.sample()
            else:
                action = np.argmax(q_table[state, :])
            new_state, reward, done, _ = env.step(action)
            q_table[state, action] += alpha * (reward + gamma * np.max(q_table[new_state, :]) - q_table[state, action])
            state = new_state
    return q_table

In this function, alpha, gamma, and epsilon are hyperparameters that control the behavior of the algorithm. Alpha is the learning rate, gamma is the discount factor, and epsilon is the exploration rate (i.e., the probability of taking a random action instead of the optimal action). The episodes parameter controls the number of times the algorithm runs.

Deep Q-learning

While Q-learning is effective for simple environments, it becomes computationally expensive for complex environments with a large number of states and actions. Deep Q-learning is a technique that uses deep neural networks to represent the Q-function, allowing the agent to learn from high-dimensional inputs such as images.

To implement Deep Q-learning, we can use the Keras library, which is an open source neural network library written in Python. The implementation involves building a neural network that takes the state as input and outputs the Q-value for each action. The network is then trained using minibatch stochastic gradient descent on a set of randomly selected experiences (i.e., tuples of state, action, reward, and next state).

Here is an example code snippet showing how to define and train a Deep Q-learning model using Keras:

from keras.models import Sequential
from keras.layers import Dense
from keras.optimizers import Adam

model = Sequential()
model.add(Dense(24, input_shape=(4,), activation='relu'))
model.add(Dense(24, activation='relu'))
model.add(Dense(2, activation='linear'))
model.compile(loss='mse', optimizer=Adam(lr=0.001))

def deep_q_learning(env, model, gamma, epsilon, epsilon_min, epsilon_decay, episodes):
    state_size = env.observation_space.shape[0]
    action_size = env.action_space.n
    score_history = []
    for episode in range(episodes):
        state = env.reset()
        done = False
        score = 0
        timestep = 0
        while not done:
            if np.random.uniform() < epsilon:
                action = env.action_space.sample()
            else:
                q_values = model.predict(state.reshape(1, state_size))[0]
                action = np.argmax(q_values)
            next_state, reward, done, _ = env.step(action)
            score += reward
            target = reward + gamma * np.amax(model.predict(next_state.reshape(1, state_size))[0])
            target_f = model.predict(state.reshape(1, state_size))
            target_f[0][action] = target
            model.fit(state.reshape(1, state_size), target_f, epochs=1, verbose=0)
            state = next_state
            timestep += 1
        score_history.append(score)
        epsilon = max(epsilon_min, epsilon_decay*epsilon)
    return model, score_history

Conclusion

In this blog post, we have explored how to implement Reinforcement Learning for Robotics using Open Source software. We started by introducing the concept of RL and discussing OpenAI Gym, an open source toolkit for developing and comparing RL algorithms. We also covered two popular RL techniques - Q-learning and Deep Q-learning - and provided example code snippets to help implement them.

While these examples are a good starting point, implementing RL for robotics is a complex and ongoing research topic. Nonetheless, by using the tools and techniques discussed in this post, readers should be able to better understand how to implement RL for their own robotics projects.

Additional Resources:

OpenAI Gym: https://gym.openai.com/docs/
Keras: https://keras.io/
Reinforcement Learning: An Introduction (book): http://incompleteideas.net/book/the-book.html