Reinforcement Learning, Pytorch, Genetic Algorithm, Multiprocessing, Evolutionary Algorithms.
dtype=torch.float) q_values = model(ob_tensor) action = np.argmax(q_values).numpy() observation_next, reward, done, info = env.step(action) observation = observation_next sum_reward += reward return sum_reward We are using torchga.model_weights_as_dict to get back the weights from the DNA and