{"push_id":2786844643,"size":1,"distinct_size":1,"ref":"refs/heads/master","head":"007208d2bb98aa11e63f0273fcee0471982f06b7","before":"170e08cf028600be10daa83e8f6642d8620dbb5c","commits":[{"sha":"007208d2bb98aa11e63f0273fcee0471982f06b7","author":{"name":"Jones Wong","email":"2a9bc5a5ce8d3e268836697c2268a9ed77e4ac96@users.noreply.github.com"},"message":"Support older version TF and Support RMSProp in Impala (#2590)\n\nto support TF version < 1.5\r\nto support rmsprop optimizer in Impala\r\n\r\nBefore TF1.5, tf.reduce_sum() and tf.reduce_max() has an argument keep_dims which has been renamed as keepdims in later versions.\r\n\r\nIn the original paper of Impala, they use rmsprop algorithm to optimize the model. We'd better also support it so that users can reproduce their experiments. Without any tuning, say that using the same hyper-parameters as AdamOptimizer, it reaches \"episode_reward_mean\": 19.083333333333332 in Pong after consume 3,610,350 samples.","distinct":true,"url":"https://api.github.com/repos/ray-project/ray/commits/007208d2bb98aa11e63f0273fcee0471982f06b7"}]}
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