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README.md

Deep Dyna-Q: Integrating Planning for Task-Completion Dialogue Policy Learning

An implementation of the
Deep Dyna-Q: Integrating Planning for Task-Completion Dialogue Policy Learning

This document describes how to run the simulation of DDQ Agent.

Content

Data

all the data is under this folder: ./src/deep_dialog/data

  • Movie Knowledge Bases
    movie_kb.1k.p --- 94% success rate (for user_goals_first_turn_template_subsets.v1.p)
    movie_kb.v2.p --- 36% success rate (for user_goals_first_turn_template_subsets.v1.p)

  • User Goals
    user_goals_first_turn_template.v2.p --- user goals extracted from the first user turn
    user_goals_first_turn_template.part.movie.v1.p --- a subset of user goals [Please use this one, the upper bound success rate on movie_kb.1k.json is 0.9765.]

  • NLG Rule Template
    dia_act_nl_pairs.v6.json --- some predefined NLG rule templates for both User simulator and Agent.

  • Dialog Act Intent
    dia_acts.txt

  • Dialog Act Slot
    slot_set.txt

Parameter

Basic setting

--agt: the agent id
--usr: the user (simulator) id
--max_turn: maximum turns
--episodes: how many dialogues to run
--slot_err_prob: slot level err probability
--slot_err_mode: which kind of slot err mode
--intent_err_prob: intent level err probability

DDQ Agent setting

--grounded: planning k steps with environment rather than world model, serving as a upper bound.
--boosted: boost the world model with examles generated by rule agent
--train_world_model: train world model on the fly

Data setting

--movie_kb_path: the movie kb path for agent side
--goal_file_path: the user goal file path for user simulator side

Model setting

--dqn_hidden_size: hidden size for RL agent
--batch_size: batch size for DDQ training
--simulation_epoch_size: how many dialogue to be simulated in one epoch
--warm_start: use rule policy to fill the experience replay buffer at the beginning
--warm_start_epochs: how many dialogues to run in the warm start

Display setting

--run_mode: 0 for display mode (NL); 1 for debug mode (Dia_Act); 2 for debug mode (Dia_Act and NL); >3 for no display (i.e. training)
--act_level: 0 for user simulator is Dia_Act level; 1 for user simulator is NL level
--auto_suggest: 0 for no auto_suggest; 1 for auto_suggest
--cmd_input_mode: 0 for NL input; 1 for Dia_Act input. (this parameter is for AgentCmd only)

Others

--write_model_dir: the directory to write the models
--trained_model_path: the path of the trained RL agent model; load the trained model for prediction purpose.

--learning_phase: train/test/all, default is all. You can split the user goal set into train and test set, or do not split (all); We introduce some randomness at the first sampled user action, even for the same user goal, the generated dialogue might be different.

Running Dialogue Agents

Train DDQ Agent with K planning steps:

python run.py --agt 9 --usr 1 --max_turn 40 
	      --movie_kb_path ./deep_dialog/data/movie_kb.1k.p 
	      --dqn_hidden_size 80 --experience_replay_pool_size 5000 
	      --episodes 500 
	      --simulation_epoch_size 100 
	      --run_mode 3 
	      --act_level 0 
	      --slot_err_prob 0.0 
	      --intent_err_prob 0.00 
	      --batch_size 16 
	      --goal_file_path ./deep_dialog/data/user_goals_first_turn_template.part.movie.v1.p 
	      --warm_start 1 --warm_start_epochs 100 
	      --planning_steps K-1 
	      --write_model_dir ./deep_dialog/checkpoints/DDQAgent
	      --torch_seed 100
	      --grounded 0
	      --boosted 1
	      --train_world_model 1

Test RL Agent with N dialogues:

python run.py --agt 9 --usr 1 --max_turn 40
	      --movie_kb_path ./deep_dialog/data/movie_kb.1k.p
	      --dqn_hidden_size 80
	      --experience_replay_pool_size 1000
	      --episodes 300 
	      --simulation_epoch_size 100
	      --write_model_dir ./deep_dialog/checkpoints/DDQAgent/
	      --slot_err_prob 0.00
	      --intent_err_prob 0.00
	      --batch_size 16
	      --goal_file_path ./deep_dialog/data/user_goals_first_turn_template.part.movie.v1.p
	      --trained_model_path ./deep_dialog/checkpoints/DDQAgent/TRAINED_MODEL
	      --run_mode 3

Experiments

To run the scripts, move the two bash files under src folder.

  1. Bash_figure_4.sh is the script for figure 4.
  2. Bash_figure_5.sh is the script for figure 5.

Evaluation

To evaluate the performance of agents, three metrics are available: success rate, average reward, average turns. Here we show the learning curve with success rate.

  1. Plotting Learning Curve python draw_learning_curve.py --result_file ./deep_dialog/checkpoints/DDQAgent/noe2e/TRAINED_MODEL.json
  2. Pull out the numbers and draw the curves in Excel

Reference

Main papers to be cited

@inproceedings{Peng2018DeepDynaQ,
  title={Deep Dyna-Q: Integrating Planning for Task-Completion Dialogue Policy Learning},
  author={Peng, Baolin and Li, Xiujun and Gao, Jianfeng and Liu, Jingjing and Wong, Kam-Fai and Su, Shang-Yu},
  booktitle={ACL},
  year={2018}
}

@article{li2016user,
  title={A User Simulator for Task-Completion Dialogues},
  author={Li, Xiujun and Lipton, Zachary C and Dhingra, Bhuwan and Li, Lihong and Gao, Jianfeng and Chen, Yun-Nung},
  journal={arXiv preprint arXiv:1612.05688},
  year={2016}
}

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