q_learning.md

Q-Learning in mlpack

Here, we demonstrate Q-Learning in mlpack through the use of a simple example, the training of a Q-Learning agent on the CartPole environment. The code has been broken into chunks for easy understanding.

#include <mlpack.hpp>

using namespace mlpack;
using namespace ens;
using namespace mlpack::rl;

We include all the necessary components of our toy example and declare namespaces for convenience.

int main()
{
  // Set up the network.
  SimpleDQN<> model(4, 64, 32, 2);

The first step in setting our Q-learning agent is to setup the network for it to use. SimpleDQN class creates a simple feed forward network with 2 hidden layers. The network constructed here has an input shape of 4 and output shape of 2. This corresponds to the structure of the CartPole environment, where each state is represented as a column vector with 4 data members (position, velocity, angle, angular velocity). Similarly, the output shape is represented by the number of possible actions, which in this case, is only 2 (foward and backward).

We can also use mlpack's ann module to set up a custom FFN network. For example, here we use a single hidden layer. However, the Q-Learning agent expects the object to have a ResetNoise method which SimpleDQN has. We can't pass mlpack's FFN network directly. Instead, we have to wrap it into SimpleDQN object.

int main()
{
  // Set up the network.
  FFN<MeanSquaredError, GaussianInitialization> network(MeanSquaredError(),
      GaussianInitialization(0, 0.001));
  network.Add<Linear>(128);
  network.Add<ReLU>();
  network.Add<Linear>(128);
  network.Add<ReLU>();
  network.Add<Linear>(2);

  SimpleDQN<> model(network);

The next step would be to setup the other components of the Q-learning agent, namely its policy, replay method and hyperparameters.

  // Set up the policy and replay method.
  GreedyPolicy<CartPole> policy(1.0, 1000, 0.1, 0.99);
  RandomReplay<CartPole> replayMethod(10, 10000);

  TrainingConfig config;
  config.StepSize() = 0.01;
  config.Discount() = 0.9;
  config.TargetNetworkSyncInterval() = 100;
  config.ExplorationSteps() = 100;
  config.DoubleQLearning() = false;
  config.StepLimit() = 200;

And now, we get to the heart of the program, declaring a Q-Learning agent.

  QLearning<CartPole, decltype(model), AdamUpdate, decltype(policy)>
      agent(config, model, policy, replayMethod);

Here, we call the QLearning constructor, passing in the type of environment, network, updater, policy and replay. We use decltype(var) as a shorthand for the variable, saving us the trouble of copying the lengthy templated type.

We pass references of the objects we created, as parameters to QLearning class.

Now, we have our Q-Learning agent agent ready to be trained on the Cart Pole environment.

  arma::running_stat<double> averageReturn;
  size_t episodes = 0;
  bool converged = true;
  while (true)
  {
    double episodeReturn = agent.Episode();
    averageReturn(episodeReturn);
    episodes += 1;

    if (episodes > 1000)
    {
      std::cout << "Cart Pole with DQN failed." << std::endl;
      converged = false;
      break;
    }

    /**
     * Reaching running average return 35 is enough to show it works.
     */
    std::cout << "Average return: " << averageReturn.mean()
        << " Episode return: " << episodeReturn << std::endl;
    if (averageReturn.mean() > 35)
      break;
  }
  if (converged)
    std::cout << "Hooray! Q-Learning agent successfully trained" << std::endl;

  return 0;
}

We set up a loop to train the agent. The exit condition is determined by the average reward which can be computed with arma::running_stat. It is used for storing running statistics of scalars, which in this case is the reward signal. The agent can be said to have converged when the average return reaches a predetermined value (i.e. > 35).

Conversely, if the average return does not go beyond that amount even after a thousand episodes, we can conclude that the agent will not converge and exit the training loop.