README.md

@@ -358,7 +358,6 @@ The default parameters work well for time-trial models where the max speed is le
 | Batch size                               | The number recent of vehicle experiences sampled at random from an experience buffer and used for updating the underlying deep-learning neural network weights.   If you have 5120 experiences in the buffer, and specify a batch size of 512, then ignoring random sampling, you will get 10 batches of experience. Each batch will be used, in turn, to update your neural network weights during training.  Use a larger batch size to promote more stable and smooth updates to the neural network weights, but be aware of the possibility that the training may be slower. |
 | Number of epochs                         |  An epoch represents one pass through all batches, where the neural network weights are updated after each batch is processed, before proceeding to the next batch.   10 epochs implies you will update the neural network weights, using all batches one at a time, but repeat this process 10 times.  Use a larger number of epochs to promote more stable updates, but expect slower training. When the batch size is small,you can use a smaller number of epochs.                                                                                                           |
 | Learning rate                            |  The learning rate controls how big the updates to the neural network weights are. Simply put, when you need to change the weights of your policy to get to the maximum cumulative reward, how much should you shift your policy.  A larger learning rate will lead to faster training, but it may struggle to converge. Smaller learning rates lead to stable convergence, but can take a long time to train.                                                                                                                                                                   |
-| Exploration                              |  This refers to the method used to determine the trade-off between exploration and exploitation. In other words, what method should we use to determine when we should stop exploring (randomly choosing actions) and when should we exploit the experience we have built up.  Since we will be using a discrete action space, you should always select CategoricalParameters.                                                                                                                                                                                                   |
 | Entropy                                  | A degree of uncertainty, or randomness, added to the probability distribution of the action space. This helps promote the selection of random actions to explore the state/action space more broadly.                                                                                                                                                                                                                                                                                                                                                                            |
 | Discount factor                          | A factor that specifies how much the future rewards contribute to the expected cumulative reward. The larger the discount factor, the farther out the model looks to determine expected cumulative reward and the slower the training. With a discount factor of 0.9, the vehicle includes rewards from an order of 10 future steps to make a move. With a discount factor of 0.999, the vehicle considers rewards from an order of 1000 future steps to make a move. The recommended discount factor values are 0.99, 0.999 and 0.9999.                                         |
 | Loss type                                | The loss type specified the type of the objective function (cost function) used to update the network weights. The Huber and Mean squared error loss types behave similarly for small updates. But as the updates become larger, the Huber loss takes smaller increments compared to the Mean squared error loss. When you have convergence problems, use the Huber loss type. When convergence is good and you want to train faster, use the Mean squared error loss type.                                                                                                      |