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I recreated some code I found online for solving the bandits problem using policy gradient. The example was in tensorflow 1.0 so I recreated it with tensorflow 2.0 using eager execution and gradient tape, however, when training the model, I have to convert the weights Tensor to a numpy array, update the weights then reassign the tf.Variable back from the numpy array. I feel this is not performant and could find a better way. Full code is here https://github.com/entrpn/reinforcement-learning/blob/master/tf2_rl/bandits.py

The main code I'm looking to improve as follows:

def train(agent,action,reward, learning_rate=0.001):
    with tf.GradientTape() as t:
        current_loss = loss(agent(action),reward)
    dW = t.gradient(current_loss,[agent.weights])
    weights_as_np = agent.weights.numpy()
    responsible_weight = agent.weights[action]
    responsible_weight_dw = np.array(dW)[0][action]

    weights_as_np[action] = weights_as_np[action] - learning_rate*responsible_weight_dw

    agent.weights.assign(tf.Variable(weights_as_np))

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If possible, try to use just tensorflow functions (put the .numpy() part out, for example), and put a @tf.function decorator on top of train() function.

The role of @tf.function is that it transforms a whole function into a tensorflow op. The whole function will be execute an order of magnitude faster than a normal Python function.

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