A computer-implemented method of training a machine learning model having a plurality of parameters, wherein the machine learning model has been trained on a first machine learning task to determine first values of the parameters of the machine learning model, and wherein the method comprises: determining, for each of the plurality of parameters, a respective measure of an importance of the parameter to the machine learning model achieving acceptable performance on the first machine learning task; obtaining training data for training the machine learning model on a second, different machine learning task; and training the machine learning model on the second machine learning task by training the machine learning model on the training data to adjust the first values of the parameters so that the machine learning model achieves an acceptable level of performance on the second machine learning task while maintaining an acceptable level of performance on the first machine learning task, wherein, during the training of the machine learning model on the second machine learning task, values of parameters that were more important in the machine learning model achieving acceptable performance on the first machine learning task are more strongly constrained to not deviate from the first values than values of parameters that were less important in the machine learning model achieving acceptable performance on the first machine learning task; wherein the machine learning model comprises a deep neural network model that includes an input layer and one or more hidden layers that each apply a non-linear transformation to a received input to generate an output, wherein the machine learning model generates the output based on the received input and on the values of the parameters of the model; and wherein the first and second machine learning tasks each comprise a reinforcement learning task in which a robotic agent interacts with a real-world environment to perform the task. wherein the machine learning model has been trained on a first machine learning task to determine first values of the parameters of the machine learning model, and wherein the method comprises: determining, for each of the plurality of parameters, a respective measure of an importance of the parameter to the machine learning model achieving acceptable performance on the first machine learning task; obtaining training data for training the machine learning model on a second, different machine learning task; and training the machine learning model on the second machine learning task by training the machine learning model on the training data to adjust the first values of the parameters so that the machine learning model achieves an acceptable level of performance on the second machine learning task while maintaining an acceptable level of performance on the first machine learning task, wherein, during the training of the machine learning model on the second machine learning task, values of parameters that were more important in the machine learning model achieving acceptable performance on the first machine learning task are more strongly constrained to not deviate from the first values than values of parameters that were less important in the machine learning model achieving acceptable performance on the first machine learning task; determining, for each of the plurality of parameters, a respective measure of an importance of the parameter to the machine learning model achieving acceptable performance on the first machine learning task; obtaining training data for training the machine learning model on a second, different machine learning task; and training the machine learning model on the second machine learning task by training the machine learning model on the training data to adjust the first values of the parameters so that the machine learning model achieves an acceptable level of performance on the second machine learning task while maintaining an acceptable level of performance on the first machine learning task, wherein, during the training of the machine learning model on the second machine learning task, values of parameters that were more important in the machine learning model achieving acceptable performance on the first machine learning task are more strongly constrained to not deviate from the first values than values of parameters that were less important in the machine learning model achieving acceptable performance on the first machine learning task; wherein the machine learning model comprises a deep neural network model that includes an input layer and one or more hidden layers that each apply a non-linear transformation to a received input to generate an output, wherein the machine learning model generates the output based on the received input and on the values of the parameters of the model; and wherein the first and second machine learning tasks each comprise a reinforcement learning task in which a robotic agent interacts with a real-world environment to perform the task.