dependabot[bot]
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35f57a3f22
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Bump tensorflow from 1.12.0 to 1.15.0
Bumps [tensorflow](https://github.com/tensorflow/tensorflow) from 1.12.0 to 1.15.0.
- [Release notes](https://github.com/tensorflow/tensorflow/releases)
- [Changelog](https://github.com/tensorflow/tensorflow/blob/master/RELEASE.md)
- [Commits](https://github.com/tensorflow/tensorflow/compare/v1.12.0...v1.15.0)
Signed-off-by: dependabot[bot] <support@github.com>
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2019-12-16 21:27:03 +00:00 |
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Brandon Rozek
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3217c76a79
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DQfD memory was adjusted to actually update the weights in the priority trees, fixing a bug in the sampling
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2019-11-17 19:50:49 -05:00 |
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Brandon Rozek
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23532fc372
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Added a way to cap the number of demonstrations that are kept in the buffer
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2019-11-17 18:29:12 -05:00 |
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Brandon Rozek
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038d406d0f
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Fixed errors with n-step returns
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2019-11-13 22:56:27 -05:00 |
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Brandon Rozek
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ed62e148d5
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Initial implementation of n-step loss
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2019-11-11 10:24:40 -05:00 |
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07c90a09f9
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Fixed scoping error with Transitions
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2019-11-04 12:09:09 -05:00 |
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ad75539776
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Implemented components necessary for Deep Q Learning from Demonstrations
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2019-11-04 07:44:39 -05:00 |
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Brandon Rozek
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17391c7467
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First draft of Deep Q Learning From Demonstrations
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2019-10-31 20:54:52 -04:00 |
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559efa38b0
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Corrected for numba deprecation
Enable the ability to render out scenes to play back data
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2019-09-19 07:57:39 -04:00 |
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a99ca66b4f
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Fixed multiprocessing with CUDA. Added entropy importance as a config option.
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2019-09-18 07:26:32 -04:00 |
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9d32a9edd1
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Merge branch 'master' of https://github.com/Brandon-Rozek/rltorch
# Conflicts:
# rltorch/agents/QEPAgent.py
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2019-09-13 20:00:13 -04:00 |
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da83f1470c
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Some work on multiprocessing evolutionary strategies from last semester
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2019-09-13 19:53:19 -04:00 |
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7aa698c349
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Added save and load functionality
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2019-09-13 19:49:04 -04:00 |
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dcf7cce30d
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Flush out print text so I can keep track of the rewards by SSHing in
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2019-09-13 19:48:51 -04:00 |
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912e3d42cb
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Added new OpenAI Baseline Wrappers
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2019-09-13 19:48:24 -04:00 |
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6d3a78cd20
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Added parallel version of ES
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2019-03-30 16:33:40 -04:00 |
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9ad63a6921
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Added license
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2019-03-30 16:32:57 -04:00 |
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b2f5220585
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Made sure the reward_batch is float across different agents
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2019-03-14 10:43:14 -04:00 |
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cdfd3ab6b9
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Playing around with QEP
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2019-03-14 00:53:51 -04:00 |
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8683b75ad9
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Corrected gamma multiplication
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2019-03-04 22:04:13 -05:00 |
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190eb1f0c4
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Correct discount_rewards function to only multiply with gamma throughout
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2019-03-04 21:59:02 -05:00 |
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11d99df977
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Added improvements to the REINFORCE algorithm
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2019-03-04 17:10:24 -05:00 |
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a59f84b446
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Cleaned up scripts, added more comments
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2019-03-04 17:09:46 -05:00 |
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e42f5bba1b
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Corrected A2C and PPO to train at the end of an episode
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2019-03-01 21:04:13 -05:00 |
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1958fc7c7e
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Corrected device when constructing fitness tensor
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2019-02-28 14:41:34 -05:00 |
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9740c40527
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d.sample returns a tensor, so we stack them to not lose the device
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2019-02-28 14:30:49 -05:00 |
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714443192d
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Added entropy into QEP (1% of loss)
Made random numbers generated in ESNetwork happen in the same device
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2019-02-28 12:17:35 -05:00 |
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76a044ace9
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Added Evolutionary Strategies Network and added more example scripts
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2019-02-27 09:52:28 -05:00 |
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26084d4c7c
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Added PPOAgent and A2CAgent to the agents submodule.
Also made some small changes to how memories are queried
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2019-02-19 20:54:30 -05:00 |
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21b820b401
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Implemented REINFORCE into the library
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2019-02-16 20:30:27 -05:00 |
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14ba64d525
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Added a single process environment runner. Also added an example for using such class.
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2019-02-16 18:15:45 -05:00 |
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736e73a1f7
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Took away explicit deleting since the next_state variable gets used in another slot
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2019-02-14 22:01:13 -05:00 |
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2caf869fd6
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Added numba as a dependency and decorated the Prioiritzed Replay function
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2019-02-14 21:42:31 -05:00 |
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19a859a4f6
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If memory or logger does not exist, then don't create those shared memory structures
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2019-02-14 21:06:44 -05:00 |
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460d4c05c1
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Fixed EnvironmentRun to be properly multiprocess.
Fixed the prioirity of bad states to be the smallest
[TODO] Make EnvironmentEpisode properly multiprocess
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2019-02-13 23:47:37 -05:00 |
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115543d201
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Fixed parallel implementation of getting experiences by using a queue
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2019-02-13 00:36:23 -05:00 |
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5094ed53af
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Updated examples to have new features
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2019-02-11 10:23:11 -05:00 |
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fe97a9b78d
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Corrected typo
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2019-02-11 00:00:34 -05:00 |
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be637664e7
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Added collections import
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2019-02-10 23:59:29 -05:00 |
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b2ab2ee132
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Implemented epsilon as a scheduler
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2019-02-10 23:56:21 -05:00 |
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04e54cddc2
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Sends importance weights into correct device for prioiritized replay
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2019-02-10 23:16:44 -05:00 |
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013d40a4f9
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Implemented Schedulers and Prioritized Replay
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2019-02-10 23:11:53 -05:00 |
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8c78f47c0c
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Added missing parameter
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2019-02-03 21:57:27 -05:00 |
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f1a52cf77b
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Added missing dependency
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2019-02-03 21:55:08 -05:00 |
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0a6f1e73f3
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Fixed another mismatched device error
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2019-02-03 00:53:13 -05:00 |
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e62385b574
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Fixed not having self.
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2019-02-03 00:51:24 -05:00 |
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03455accc8
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Attempting to see if this fixes the mismatched devices error
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2019-02-03 00:49:47 -05:00 |
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9cd3625fd3
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Made sure everything went to their appropriate devices
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2019-02-03 00:45:14 -05:00 |
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39643f04e1
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Simplified in place guassian code
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2019-02-03 00:31:35 -05:00 |
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a03abe2bb1
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Initial Commit
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2019-01-31 23:34:32 -05:00 |
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