"Emergent Communication through Negotiation"

Reproduce https://openreview.net/forum?id=Hk6WhagRW&noteId=Hk6WhagRW , "Emergent Communication through Negotation", ICLR 2018 anonymous submission.

To install

• install pytorch 0.2, https://pytorch.org
• download this repo, git clone https://github.com/asappinc/emergent_comms_negotiation

To run

python ecn.py [--disable-comms] [--disable-proposal] [--disable-prosocial] [--enable-cuda] [--term-entropy-reg 0.5] [--utterance-entropy-reg 0.0001] [--proposal-entropy-reg 0.01] [--model-file model_saves/mymodel.dat] [--name gpu3box]

Where options are:

• --enable-cuda: use NVIDIA GPU, instead of CPU
• --disable-comms: disable the comms channel
• --disable-proposal: disable the proposal channel (ie agent can create proposals, but other agent cant see them)
• --disable-prosocial: disable prosocial reward
• --term-entropy-reg VALUE: termination policy entropy regularization
• --utterance-entorpy-reg VALUE: utterance policy entropy regularization
• --proposal-entropy-reg VALUE: proposal policy entropy regularization
• --model-file models_saves/FILENAME: where to save the model to, and where to look for it on startup
• --name NAME: this is used in the logfile name, just to make it easier to find/distinguish logfiles, no other purpose

Stdout layout

eg if we have:

   000000 4:4/0 7:5/5 9:4/4
                                      000000 4:5/0 6:1/5 7:2/4
   000000 4:0/0 7:0/5 9:1/4
                                      ACC
  r: 0.91

Then:

• each of the first 4 lines here is the action of a single agent
• the ACC line is the agent accepting previous proposal
• each proposal line is laid out as:

  [utterance]   [utility 0]:[proposal 0]/[pool 0] ... etc ...

• if the agents run out of time, last line will be [out of time]

One negotation is printed out every 3 seconds or so, using the training set; the other negotations are executed silently. There is no test set for now.

Results so far, summary

Notes:

• prosocial runs all use termreg=0.5, uttreg=0.0001, propreg=0.01
• self-interested run uses: termreg=0.05, uttreg=0.0001, propreg=0.005

Scenario details

Training curves

proposal, comms, prosocial

Three training runs, identical settings:

Proposal, no comms, prosocial

No proposal, comms, prosocial

No proposal, no comms, prosocial

Proposal, comms, no social

Run 1, same entropy regularization as prosocial graphs:

Run 2, with reduced entropy regularization:

Unit tests

• install pytest, ie conda install -y pytest, and then:

py.test -svx

• there are also some additional tests in:

python net_tests.py

(which allow close examination of specific parts of the network, policies, and so on; but which arent really 'unit-tests' as such, since neither termination criteria, nor success criteria)

Plotting graphs

Assumptions:

• running the training on remote Ubuntu 16.04 instances
  • ssh access, as user ubuntu, to these instances
  • remote has home directory /home/ubuntu
  • logs are stored in subdirectory logs of current local directory
  • the location of logs relative to ~ is identical on local computer and remote computer

Setup/configuration:

• copy instances.yaml.templ to ~/instances.yaml, on your own machine
  • configure ~/instances.yaml with:
    • name and ip of each instance (names are arbitrary)
    • the path to your private sshkey, that can access these instances

Procedure

• run:

python merge.py --hostname [name in instances.yaml] [--logfile logs/log_20171104_1234.log] \
    [--title 'my graph title'] [--y-min 75 --y-max 85]

This will:

• rsync the logs from the remote instance identified by --hostname
• if --logfile is specified, load the results from that logfile
  • else, will look for the most recent logfile, ordered by name
• plots the graph into /tmp/out-reward.png