rn | sarsa
spec.gamma = 0.9; // discount factor, [0, 1)
spec.epsilon = 0.2; // initial epsilon for epsilon-greedy policy, [0, 1)
spec.alpha = 0.005; // value function learning rate
spec.experience_add_every = 5; // number of time steps before we add another experience to replay memory
spec.experience_size = 10000; // size of experience
spec.learning_steps_per_iteration = 5;
spec.tderror_clamp = 1.0; // for robustness
spec.num_hidden_units = 100 // number of neurons in hidden layer
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