Comments (6)
Very sorry - I think its training fine with GPU now - forgot to rebuild all/delete old build directory?
I'm not very good with computers :(
from dqn-in-the-caffe.
Yes, you need to rebuild it if you change CPU_ONLY option.
./dqn FLAGS_gpu true
is not a correct way to change the values of the flags. If you want to tun on the gpu flag, you should just write ./dqn -gpu
. If you do so, you don't have to modify dqn_main.cpp.
from dqn-in-the-caffe.
Thanks alot. Really nice code, very clear and compact :)
I'm writing everything down, so if you want to post it just let me know?
May I ask how often does caffe
, save the network, or just give some feedback on the lost function and training of the network? I'm new to caffe
, and still working through your code. I guess I need to look into how to parse stuff into caffe
as well?
I'm also working with some really good guys on a Python/Theano/RL-glue implementation. Testing, network saving and output/control in general a more formal process, (because of RL-glue), and easier to follow, for NOOBs
at least. We have a forum - it would be great if you joined and gave us some direction, if you have the time?
https://groups.google.com/forum/#!forum/deep-q-learning
https://github.com/spragunr/deep_q_rl
Finally I'm also trying to implement a very lightweight version, in Lua/Torch. This would be a bit more closer to Deepminds implementation as they've made their emulator public now,
https://github.com/deepmind/xitari
https://github.com/deepmind/alewrap
Cheers 👍
from dqn-in-the-caffe.
Currently the network parameters are saved into a file after every 50000 iterations. You can change the interval by changing snapshot
param in dqn_solver.prototxt
.
Thank you for letting me know other projects. The forum looks great.
from dqn-in-the-caffe.
Very warm welcome, great to have you as part of the gang :)
Sorry, yes I see, from http://caffe.berkeleyvision.org/tutorial/solver.html
Snapshotting is configured by:
/ The snapshot interval in iterations.
snapshot: 5000
/ File path prefix for snapshotting model weights and solver state.
/ Note: this is relative to the invocation of the caffe
utility, not the
/ solver definition file.
snapshot_prefix: "/path/to/model"
/ Snapshot the diff along with the weights. This can help debugging training
/ but takes more storage.
snapshot_diff: false
/ A final snapshot is saved at the end of training unless
/ this flag is set to false. The default is true.
snapshot_after_train: true
in the solver definition prototxt.
I like caffe
it's really well documented.
A bit heavy going at first, but a good investment!
from dqn-in-the-caffe.
Hi I have another question.
If I set the GPU mode in solver prototxt, Do I need to set the mode of GPU in dqn_mian again?
from dqn-in-the-caffe.
Related Issues (20)
- Parameters used to learn Pong? HOT 2
- Linking problems
- video file
- eltwise_layer shape check failed HOT 12
- undefined references to `DisplayScreen::display_screen(MediaSource const&) HOT 4
- Source for pong.bin
- how to build and get_start?
- Is there a python implementation or interface for the same?
- where can download the Arcade Learning Environment? HOT 1
- Screen display requires directive __USE_SDL to be defined
- cannot find #include "caffe/proto/caffe.pb.h"
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- Use One network or Two network?
- fatal error: caffe/proto/caffe.pb.h: No such file or directory #include "caffe/proto/caffe.pb.h" HOT 9
- A doubt at line 350 in dqn.cpp HOT 5
- no good results HOT 27
- Running Problem HOT 3
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