@inproceedings{DBLP:conf/icml/YaratsL18,
  author    = {Denis Yarats and
               Mike Lewis},
  title     = {Hierarchical Text Generation and Planning for Strategic Dialogue},
  booktitle = {Proceedings of the 35th International Conference on Machine Learning,
               {ICML} 2018, Stockholmsm{\"{a}}ssan, Stockholm, Sweden, July
               10-15, 2018},
  pages     = {5587--5595},
  year      = {2018},
  crossref  = {DBLP:conf/icml/2018},
  url       = {http://proceedings.mlr.press/v80/yarats18a.html},
  timestamp = {Fri, 13 Jul 2018 14:58:25 +0200},
  biburl    = {https://dblp.org/rec/bib/conf/icml/YaratsL18},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

# Perspective of Agent 1
<input> 1 4 4 1 1 2 </input>
<dialogue> THEM: i would like 4 hats and you can have the rest . <eos> YOU: deal <eos> THEM: <selection> </dialogue>
<output> item0=1 item1=0 item2=1 item0=0 item1=4 item2=0 </output> 
<partner_input> 1 0 4 2 1 2 </partner_input>

# Perspective of Agent 2
<input> 1 0 4 2 1 2 </input>
<dialogue> YOU: i would like 4 hats and you can have the rest . <eos> THEM: deal <eos> YOU: <selection> </dialogue>
<output> item0=0 item1=4 item2=0 item0=1 item1=0 item2=1 </output>
<partner_input> 1 4 4 1 1 2 </partner_input>

# Install anaconda
conda create -n py30 python=3 anaconda
# Activate environment
source activate py30
# Install PyTorch
conda install pytorch torchvision cuda90 -c pytorch
# Install Visdom if you want to use visualization
pip install visdom

python train.py \
--cuda \
--bsz 16 \
--clip 2.0 \
--decay_every 1 \
--decay_rate 5.0 \
--domain object_division \
--dropout 0.1 \
--init_range 0.2 \
--lr 0.001 \
--max_epoch 7 \
--min_lr 1e-05 \
--model_type selection_model \
--momentum 0.1 \
--nembed_ctx 128 \
--nembed_word 128 \
--nhid_attn 128 \
--nhid_ctx 64 \
--nhid_lang 128 \
--nhid_sel 128 \
--nhid_strat 256 \
--unk_threshold 20 \
--skip_values \
--sep_sel \
--model_file selection_model.th

python train.py \
--cuda \
--bsz 16 \
--clip 0.5 \
--decay_every 1 \
--decay_rate 5.0 \
--domain object_division \
--dropout 0.1 \
--model_type rnn_model \
--init_range 0.2 \
--lr 0.001 \
--max_epoch 30 \
--min_lr 1e-07 \
--momentum 0.1 \
--nembed_ctx 64 \
--nembed_word 256 \
--nhid_attn 64 \
--nhid_ctx 64 \
--nhid_lang 128 \
--nhid_sel 128 \
--sel_weight 0.6 \
--unk_threshold 20 \
--sep_sel \
--model_file rnn_model.th

python train.py \
--cuda \
--bsz 16 \
--clip 2.0 \
--decay_every 1 \
--decay_rate 5.0 \
--domain object_division \
--dropout 0.2 \
--init_range 0.3 \
--lr 0.001 \
--max_epoch 15 \
--min_lr 1e-05 \
--model_type latent_clustering_model \
--momentum 0.1 \
--nembed_ctx 64 \
--nembed_word 256 \
--nhid_ctx 64 \
--nhid_lang 256 \
--nhid_sel 128 \
--nhid_strat 256 \
--unk_threshold 20 \
--num_clusters 50 \
--sep_sel \
--skip_values \
--nhid_cluster 256 \
--selection_model_file selection_model.th \
--model_file clustering_model.th

python train.py \
--cuda \
--bsz 16 \
--clip 2.0 \
--decay_every 1 \
--decay_rate 5.0 \
--domain object_division \
--dropout 0.1 \
--init_range 0.2 \
--lr 0.001 \
--max_epoch 15 \
--min_lr 1e-05 \
--model_type latent_clustering_language_model \
--momentum 0.1 \
--nembed_ctx 64 \
--nembed_word 256 \
--nhid_ctx 64 \
--nhid_lang 256 \
--nhid_sel 128 \
--nhid_strat 256 \
--unk_threshold 20 \
--num_clusters 50 \
--sep_sel \
--nhid_cluster 256 \
--skip_values \
--selection_model_file selection_model.th \
--cluster_model_file clustering_model.th \
--model_file clustering_language_model.th

python train.py \
--cuda \
--bsz 16 \
--clip 2.0 \
--decay_every 1 \
--decay_rate 5.0 \
--domain object_division \
--dropout 0.2 \
--init_range 0.3 \
--lr 0.001 \
--max_epoch 10 \
--min_lr 1e-05 \
--model_type latent_clustering_prediction_model \
--momentum 0.2 \
--nembed_ctx 64 \
--nembed_word 256 \
--nhid_ctx 64 \
--nhid_lang 256 \
--nhid_sel 128 \
--nhid_strat 256 \
--unk_threshold 20 \
--num_clusters 50 \
--sep_sel \
--selection_model_file selection_model.th \
--lang_model_file clustering_language_model.th \
--model_file full_model.th

python selfplay.py \
--cuda \
--alice_model_file rnn_model.th \
--bob_model_file rnn_model.th \
--context_file data/negotiate/selfplay.txt  \
--temperature 0.5 \
--selection_model_file selection_model.th

================================================================================
Alice : book=(count:3 value:1) hat=(count:1 value:5) ball=(count:1 value:2)
Bob   : book=(count:3 value:1) hat=(count:1 value:1) ball=(count:1 value:6)
--------------------------------------------------------------------------------
Alice : i would like the hat and the ball . <eos>
Bob   : i need the ball and the hat <eos>
Alice : i can give you the ball and one book . <eos>
Bob   : i can't make a deal without the ball <eos>
Alice : okay then i will take the hat and the ball <eos>
Bob   : okay , that's fine . <eos>
Alice : <selection>
Alice : book=0 hat=1 ball=1 book=3 hat=0 ball=0
Bob   : book=3 hat=0 ball=0 book=0 hat=1 ball=1
--------------------------------------------------------------------------------
Agreement!
Alice : 7 points
Bob   : 3 points
--------------------------------------------------------------------------------
dialog_len=4.47 sent_len=6.93 agree=86.67% advantage=3.14 time=2.069s comb_rew=10.93 alice_rew=6.93 alice_sel=60.00% alice_unique=26 bob_rew=4.00 bob_sel=40.00% bob_unique=25 full_match=0.78 
--------------------------------------------------------------------------------
debug: 3 1 1 5 1 2 item0=0 item1=1 item2=1
debug: 3 1 1 1 1 6 item0=3 item1=0 item2=0
================================================================================

python reinforce.py \
--cuda \
--alice_model_file rnn_model.th \
--bob_model_file rnn_model.th \
--output_model_file rnn_rl_model.th \
--context_file data/negotiate/selfplay.txt  \
--temperature 0.5 \
--verbose \
--log_file rnn_rl.log \
--sv_train_freq 4 \
--nepoch 4 \
--selection_model_file selection_model.th  \
--rl_lr 0.00001 \
--rl_clip 0.0001 \
--sep_sel