Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for training a neural network used to select actions performed by a reinforcement learning agent interacting with an environment. In one aspect, a method includes maintaining a replay memory, where the replay memory stores pieces of experience data generated as a result of the reinforcement learning agent interacting with the environment. Each piece of experience data is associated with a respective expected learning progress measure that is a measure of an expected amount of progress made in the training of the neural network if the neural network is trained on the piece of experience data. The method further includes selecting a piece of experience data from the replay memory by prioritizing for selection pieces of experience data having relatively higher expected learning progress measures and training the neural network on the selected piece of experience data.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A method for training a neural network used to select actions performed by a reinforcement learning agent interacting with an environment by performing actions that cause the environment to transition states, the method comprising: maintaining a replay memory, the replay memory storing pieces of experience data generated as a result of the reinforcement learning agent interacting with the environment, a plurality of the pieces of experience data each having a respective expected learning progress measure that is a measure of an expected amount of progress made in the training of the neural network if the neural network is trained on the piece of experience data; selecting a piece of experience data from the replay memory by prioritizing for selection pieces of experience data having relatively higher expected learning progress measures; and training the neural network on the selected piece of experience data. 2. The method of claim 1, wherein selecting the piece of experience data comprises: determining a respective probability for each piece of experience data such that pieces of experience data having higher expected learning progress measures have higher probabilities than pieces of experience data having relatively lower expected learning progress measures; and sampling a piece of experience data in accordance with the determined probabilities. 3. The method of claim 2, wherein the probability P(i) for a piece of experience data i satisfies: P  ( i ) = p i α ∑ k   p k α where α is a predetermined constant, k ranges across the pieces of experience data in the replay memory, and pi is a priority for the piece of experience data i derived from the expected learning progress measure for the piece of experience data i. 4. The method of claim 3, wherein the priority is the expected learning measure plus a constant value. 5. The method of claim 3, wherein the priority is a fraction having a predetermined positive value as a numerator and a rank of the piece of experience data i in a ranking of the pieces of experience data in the replay memory according to their expected learning progress measures as a denominator. 6. The method of claim 3, wherein the priority is set to a maximum value for a piece of experience data that has not yet been used in training. 7. The method of claim 1, wherein each piece of experience data is an experience tuple that comprises a respective current observation characterizing a respective current state of the environment, a respective current action performed by the agent in response to the current observation, a respective next state characterizing a respective next state of the environment, and a reward received in response to the agent performing the current action. 8. The method of claim 7, wherein training the neural network on the selected piece of experience data comprises: determining a temporal difference learning error for the selected experience tuple; and using the temporal difference learning error in adjusting values of the parameters of the neural network. 9. The method of claim 8, wherein using the temporal difference learning error in adjusting the values of the parameters comprises: determining a weight for the temporal difference learning error using the expected learning progress measure for the selected experience tuple; adjusting the temporal difference learning error using the weight; and using the adjusted temporal difference learning error as a target error for adjusting the values of the parameters of the neural network. 10. The method of claim 8, further comprising annealing an exponent used in computing the weight during the training of the neural network. 11. The method of claim 7, wherein the expected learning progress measure for each experience tuple in the replay memory is an absolute value of a temporal difference learning error determined for the experience tuple the preceding time the experience tuple was used in training the neural network. 12. The method of claim 7, wherein the expected learning progress measure for each experience tuple in the replay memory is a derivative of an absolute value of a temporal difference learning error determined for the experience tuple the preceding time the experience tuple was used in training the neural network. 13. The method of claim 7, wherein the expected learning progress measure for each experience tuple in the replay memory is a norm of an induced weight-change by using the experience tuple to train the neural network. 14. The method of claim 1, further comprising: determining when to delete pieces of experience data from the replay memory using the expected learning progress measures. 15. A system comprising one or more computers and one or more storage devices storing instructions that are operable, when executed by the one or more computers, to cause the one or more computers to perform operations for a method for training a neural network used to select actions performed by a reinforcement learning agent interacting with an environment by performing actions that cause the environment to transition states, the method comprising: maintaining a replay memory, the replay memory storing pieces of experience data generated as a result of the reinforcement learning agent interacting with the environment, a plurality of the pieces of experience data each having a respective expected learning progress measure that is a measure of an expected amount of progress made in the training of the neural network if the neural network is trained on the piece of experience data; selecting a piece of experience data from the replay memory by prioritizing for selection pieces of experience data having relatively higher expected learning progress measures; and training the neural network on the selected piece of experience data. 16. The system of claim 15, wherein selecting the piece of experience data comprises: determining a respective probability for each piece of experience data such that pieces of experience data having higher expected learning progress measures have higher probabilities than pieces of experience data having relatively lower expected learning progress measures; and sampling a piece of experience data in accordance with the determined probabilities. 17. The system of claim 16, wherein the probability P(i) for a piece of experience data i satisfies: P  ( i ) = p i α ∑ k   p k α where α is a predetermined constant, k ranges across the pieces of experience data in the replay memory, and pi is a priority for the piece of experience data i derived from the expected learning progress measure for the piece of experience data i. 18. The system of claim 15, wherein each piece of experience data is an experience tuple that comprises a respective current observation characterizing a respective current state of the environment, a respective current action performed by the agent in response to the current observation, a respective next state characterizing a respective next state of the environment, and a reward received in response to the agent performing the current action. 19. The system of claim 18, wherein training the neural network on the selected piece of experience data comprises: determining a temporal difference learning error for the selected experience tuple; and using the temporal difference learning error in adjusting values of the parameters of the neural network. 20. A computer storage medium encoded with instructions that, when executed by one or more computers, cause the one or more computers to perform operations for a method for training a neural network used to select actions performed by a reinforcement learning agent interacting with an environment by performing actions that cause the environment to transition states, the method comprising: maintaining a replay memory, the replay memory storing pieces of experience data generated as a result of the reinforcement learning agent interacting with the environment, a plurality of the pieces of experience data each having a respective expected learning progress measure that is a measure of an expected amount of progress made in the training of the neural network if the neural network is trained on the piece of experience data; selecting a piece of experience data from the replay memory by prioritizing for selection pieces of experience data having relatively higher expected learning progress measures; and training the neural network on the selected piece of experience data.