The PTAN CartPole solver

Let’s now take the PTAN classes (without Ignite so far) and try to combine everything to solve our first environment: CartPole. The complete code is in Chapter07/06_cartpole.py. I will show only the important parts of the code related to the material that we have just covered.

First, we create the NN (the simple two-layer feed-forward NN that we used for CartPole before) and target the NN epsilon-greedy action selector and DQNAgent. Then, the experience source and replay buffer are created:

    net = Net(obs_size, HIDDEN_SIZE, n_actions) 
    tgt_net = ptan.agent.TargetNet(net) 
    selector = ptan.actions.ArgmaxActionSelector() 
    selector = ptan.actions.EpsilonGreedyActionSelector(epsilon=1, selector=selector) 
    agent = ptan.agent.DQNAgent(net, selector) 
    exp_source = ptan.experience.ExperienceSourceFirstLast(env, agent, gamma=GAMMA) 
    buffer = ptan.experience.ExperienceReplayBuffer(exp_source, buffer_size...