During heatup we may want to add agent-generated-noise (i.e. not "simple" random noise).
This is enabled by setting 'heatup_using_network_decisions' to True. For example:
agent_params = DDPGAgentParameters()
agent_params.algorithm.heatup_using_network_decisions = True
The fix ensures that the correct noise is added not just while in the TRAINING phase, but
also during the HEATUP phase.
No one has enabled 'heatup_using_network_decisions' yet, which explains why this problem
arose only now (in my configuration I do enable 'heatup_using_network_decisions').
Currently in rollout worker, we call restore_checkpoint repeatedly to load the latest model in memory. The restore checkpoint functions calls checkpoint_saver. Checkpoint saver uses GlobalVariablesSaver which does not release the references of the previous model variables. This leads to the situation where the memory keeps on growing before crashing the rollout worker.
This change avoid using the checkpoint saver in the rollout worker as I believe it is not needed in this code path.
Also added a test to easily reproduce the issue using CartPole example. We were also seeing this issue with the AWS DeepRacer implementation and the current implementation avoid the memory leak there as well.
1. Having the standard checkpoint prefix in order for the data store to grab it, and sync it to S3.
2. Removing the reference to Redis so that it won't try to pickle that in.
3. Enable restoring a checkpoint into a single-worker run, which was saved by a single-node-multiple-worker run.
* Adding checkpointing framework as well as mxnet checkpointing implementation.
- MXNet checkpoint for each network is saved in a separate file.
* Adding checkpoint restore for mxnet to graph-manager
* Add unit-test for get_checkpoint_state()
* Added match.group() to fix unit-test failing on CI
* Added ONNX export support for MXNet
* Enable setting the data store factory in Graph manager
This change enables us to use custom data store for storing and retrieving models.
We currently need this to have use a data store that loads temporary AWS credentials
from disk before calling store or load operations.
* Removed data store factory and introduced data store as a attribute
* Allow arbitrary dimensional observation (non vector or image)
* Added creating PlanarMapsObservationSpace to GymEnvironment when number of channels is not 1 or 3
