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Last Commit
Mar. 23, 2019
May. 24, 2018



This repository contains the code developed in TensorFlow for the following paper:

If you used this code, please kindly consider citing the following paper:

 title={Deep Reinforcement Learning For Sequence to Sequence Models},
 author={Keneshloo, Yaser and Shi, Tian and Ramakrishnan, Naren and Reddy, Chandan K.},
 journal={arXiv preprint arXiv:1805.09461},

Table of Contents



In recent years, sequence-to-sequence (seq2seq) models are used in a variety of tasks from machine translation, headline generation, text summarization, speech to text, to image caption generation. The underlying framework of all these models are usually a deep neural network which contains an encoder and decoder. The encoder processes the input data and a decoder receives the output of the encoder and generates the final output. Although simply using an encoder/decoder model would, most of the time, produce better result than traditional methods on the above-mentioned tasks, researchers proposed additional improvements over these sequence to sequence models, like using an attention-based model over the input, pointer-generation models, and self-attention models. However, all these seq2seq models suffer from two common problems: 1) exposure bias and 2) inconsistency between train/test measurement. Recently a completely fresh point of view emerged in solving these two problems in seq2seq models by using methods in Reinforcement Learning (RL). In these new researches, we try to look at the seq2seq problems from the RL point of view and we try to come up with a formulation that could combine the power of RL methods in decision-making and sequence to sequence models in remembering long memories. In this paper, we will summarize some of the most recent frameworks that combines concepts from RL world to the deep neural network area and explain how these two areas could benefit from each other in solving complex seq2seq tasks. In the end, we will provide insights on some of the problems of the current existing models and how we can improve them with better RL models. We also provide the source code for implementing most of the models that will be discussed in this paper on the complex task of abstractive text summarization.



  • Use Python 2.7

Python requirements can be installed as follows:

pip install -r python_requirements.txt


  • Tensorflow 1.10.1


  • CUDA 9
  • Cudnn 7.1


CNN/Daily Mail dataset

Newsroom dataset

We have provided helper codes to download the cnn-dailymail dataset and pre-process this dataset and newsroom dataset. Please refer to this link to access them.

We saw a large improvement on the ROUGE measure by using our processed version of these datasets in the summarization results, therefore, we strongly suggest using these pre-processed files for all the training.

Running Experiments

This code is a general framework for a variety of different modes that supports the following features:

  1. Scheduled Sampling, Soft-Scheduled Sampling, and End2EndBackProp.
  2. Policy-Gradient w. Self-Critic learning and temporal attention and intra-decoder attention:
    1. Following A Deep Reinforced Model for Abstractive Summarization
  3. Actor-Critic model through DDQN and Dueling network based on these papers:
    1. Deep Reinforcement Learning with Double Qlearning
    2. Dueling Network Architectures for Deep Reinforcement Learning
    3. An ActorCritic Algorithm for Sequence Prediction

Scheduled Sampling, Soft-Scheduled Sampling, and End2EndBackProp

Bengio et al. proposed the idea of scheduled sampling for avoiding exposure bias problem. Recently, Goyal et al. proposed a differentiable relaxtion of this method, by using soft-argmax rather hard-argmax, that solves the back-propagation error that exists in this model. Also, Ranzato et al. proposed another simple model called End2EndBackProp for avoiding exposure bias problem. To train a model based on each of these papers, we provide different flags as follows:

Parameter Default Description
scheduled_sampling False whether to do scheduled sampling or not
sampling_probability 0 epsilon value for choosing ground-truth or model output
fixed_sampling_probability False Whether to use fixed sampling probability or adaptive
hard_argmax True Whether to use soft argmax or hard argmax
greedy_scheduled_sampling False Whether to use greedy or sample for the output, True means greedy
E2EBackProp False Whether to use E2EBackProp algorithm to solve exposure bias
alpha 1 soft argmax argument
Scheduled Sampling using Hard-Argmax and Greedy selection (Bengio et al.):
CUDA_VISIBLE_DEVICES=0 python src/ --mode=train --data_path=$HOME/data/cnn_dm/finished_files/chunked/train_* --vocab_path=$HOME/data/cnn_dm/finished_files/vocab --log_root=$HOME/working_dir/cnn_dm/RLSeq2Seq/ --exp_name=scheduled-sampling-hardargmax-greedy --batch_size=80 --max_iter=40000 --scheduled_sampling=True --sampling_probability=2.5E-05 --hard_argmax=True --greedy_scheduled_sampling=True
Scheduled Sampling using Soft-Argmax and Sampling selection (Goyal et al.):
CUDA_VISIBLE_DEVICES=0 python src/ --mode=train --data_path=$HOME/data/cnn_dm/finished_files/chunked/train_* --vocab_path=$HOME/data/cnn_dm/finished_files/vocab --log_root=$HOME/working_dir/cnn_dm/RLSeq2Seq/ --exp_name=scheduled-sampling-softargmax-sampling --batch_size=80 --max_iter=40000 --scheduled_sampling=True --sampling_probability=2.5E-05 --hard_argmax=False --greedy_scheduled_sampling=False --alpha=10
End2EndBackProp (Ranzato et al.):
CUDA_VISIBLE_DEVICES=0 python src/ --mode=train --data_path=$HOME/data/cnn_dm/finished_files/chunked/train_* --vocab_path=$HOME/data/cnn_dm/finished_files/vocab --log_root=$HOME/working_dir/cnn_dm/RLSeq2Seq/ --exp_name=scheduled-sampling-end2endbackprop --batch_size=80 --max_iter=40000 --scheduled_sampling=True --sampling_probability=2.5E-05 --hard_argmax=True --E2EBackProp=True --k=4

Policy-Gradient w. Self-Critic learning and temporal attention and intra-decoder attention

Parameter Default Description
rl_training False Start policy-gradient training
convert_to_reinforce_model False Convert a pointer model to a reinforce model. Turn this on and run in train mode. Your current training model will be copied to a new version (same name with _cov_init appended) that will be ready to run with coverage flag turned on, for the coverage training stage.
intradecoder False Use intradecoder attention or not
use_temporal_attention True Whether to use temporal attention or not
matrix_attention False Use matrix attention, Eq. 2 in
eta 0 RL/MLE scaling factor, 1 means use RL loss, 0 means use MLE loss
fixed_eta False Use fixed value for eta or adaptive based on global step
gamma 0.99 RL reward discount factor
reward_function rouge_l/f_score Either bleu or one of the rouge measures (rouge_1/f_score, rouge_2/f_score,rouge_l/f_score)

Paulus et al. proposed a self-critic policy-gradient model for abstractive text summarization. The following figure represents how this method works and how we implemented this method:


To replicate their experiment, we can use the following set of processes:

Pre-Training using only MLE loss with intradecoder attention and temporal attention
CUDA_VISIBLE_DEVICES=0 python src/ --mode=train --data_path=$HOME/data/cnn_dm/finished_files/chunked/train_* --vocab_path=$HOME/data/cnn_dm/finished_files/vocab --log_root=$HOME/working_dir/cnn_dm/RLSeq2Seq/ --exp_name=intradecoder-temporalattention-withpretraining --batch_size=80 --max_iter=20000 --use_temporal_attention=True --intradecoder=True --rl_training=False
Evaluation the pre-trained model on validation data

Here, we use a different GPU for evaluation, but we can use the same GPU if we decrease the number of batches. In our implementation, we use a batch size of 8 for evaluation but for each eval step, we iterate over the validation dataset 100 times. This is similar to finding the evaluation error on a batch size of 800. This will help to decrease the memory required by the evaluation process and provide options for running both training and eval on one GPU.

CUDA_VISIBLE_DEVICES=1 python src/ --mode=eval --data_path=$HOME/data/cnn_dm/finished_files/chunked/val_* --vocab_path=$HOME/data/cnn_dm/finished_files/vocab --log_root=$HOME/working_dir/cnn_dm/RLSeq2Seq/ --exp_name=intradecoder-temporalattention-withpretraining --batch_size=8 --use_temporal_attention=True --intradecoder=True --rl_training=False
Activating MLE+RL training

As suggested by Paulus et al, we use a linear transition from Cross-Entropy loss to RL loss so that in the end we completely rely on RL loss to train the model. The parameter eta controls this transition. We set eta to be eta = 1/(max RL iteration).

First, add required training parameter to the model:

CUDA_VISIBLE_DEVICES=0 python src/ --mode=train --data_path=$HOME/data/cnn_dm/finished_files/chunked/train_* --vocab_path=$HOME/data/cnn_dm/finished_files/vocab --log_root=$HOME/working_dir/cnn_dm/RLSeq2Seq/ --exp_name=intradecoder-temporalattention-withpretraining --batch_size=80 --max_iter=40000 --intradecoder=True --use_temporal_attention=True --eta=2.5E-05 --rl_training=True --convert_to_reinforce_model=True

Then, start running the model with MLE+RL training loss:

CUDA_VISIBLE_DEVICES=0 python src/ --mode=train --data_path=$HOME/data/cnn_dm/finished_files/chunked/train_* --vocab_path=$HOME/data/cnn_dm/finished_files/vocab --log_root=$HOME/working_dir/cnn_dm/RLSeq2Seq/ --exp_name=intradecoder-temporalattention-withpretraining --batch_size=80 --max_iter=40000 --intradecoder=True --use_temporal_attention=True --eta=2.5E-05 --rl_training=True
Evaluating MLE+RL training on validation data
CUDA_VISIBLE_DEVICES=1 python src/ --mode=eval --data_path=$HOME/data/cnn_dm/finished_files/chunked/val_* --vocab_path=$HOME/data/cnn_dm/finished_files/vocab --log_root=$HOME/working_dir/cnn_dm/RLSeq2Seq/ --exp_name=intradecoder-temporalattention-withpretraining --batch_size=8 --use_temporal_attention=True --intradecoder=True --rl_training=True
Start decoding the trained model

We use ROUGE as the evaluation metrics.

CUDA_VISIBLE_DEVICES=0 python src/ --mode=decode --data_path=$HOME/data/cnn_dm/finished_files/chunked/test_* --vocab_path=$HOME/data/cnn_dm/finished_files/vocab --log_root=$HOME/working_dir/cnn_dm/RLSeq2Seq/ --exp_name=intradecoder-temporalattention-withpretraining --rl_training=True --intradecoder=True --use_temporal_attention=True --single_pass=1 --beam_size=4 --decode_after=0

Actor-Critic model through DDQN and Dueling network

Parameter Default Description
ac_training False Use Actor-Critic learning by DDQN.
dqn_scheduled_sampling False Whether to use scheduled sampling to use estimates of DDQN model vs the actual Q-estimates values
dqn_layers 512,256,128 DDQN dense hidden layer size. It will create three dense layers with 512, 256, and 128 size
dqn_replay_buffer_size 100000 Size of the replay buffer
dqn_batch_size 100 Batch size for training the DDQN model
dqn_target_update 10000 Update target Q network every 10000 steps
dqn_sleep_time 2 Train DDQN model every 2 seconds
dqn_gpu_num 1 GPU number to train the DDQN
dueling_net True Whether to use Duelling Network to train the model
dqn_polyak_averaging True Whether to use Polyak averaging to update the target Q network parameters: Psi^{prime} = (tau * Psi^{prime})+ (1-tau)*Psi
calculate_true_q False Whether to use true Q-values to train DDQN or use DDQN's estimates to train it
dqn_pretrain False Pretrain the DDQN network with fixed Actor model
dqn_pretrain_steps 10000 Number of steps to pre-train the DDQN

The general framework for the Actor-Critic model is as follows:


In our implementation, the Actor is the pointer-generator model and the Critic is a regression model that minimizes the Q-value estimation using Double Deep Q Network (DDQN). The code is implemented such that the DDQN training is on a different thread from the main thread and we collect experiences for this network asynchronously from the Actor model. Therefore, for each batch, we collect (batch_size * max_dec_steps) states for the DDQN training. We implemented the prioritized replay buffer. and during DDQN training we always select our mini batches such that they contain experiences that have the best partial reward according to the ground-truth summary. We added an option of training DDQN based on true Q-estimation and offered a scheduled-sampling process for training this network. Please note that training DDQN using true Q-estimation will significantly reduce the speed of training, due to the collection of true Q-values. Therefore, we suggest to only activate this for a few iterations. As suggested by Bahdanau et al. it is also good to use a fixed pre-trained Actor to pre-train the Critic model first and then start training both models, simultaneously. For instance, we can use the following set of codes to run a similar experiment as Bahdanau et al.:

Pre-Training the Actor using only MLE loss
CUDA_VISIBLE_DEVICES=0 python src/ --mode=train --data_path=$HOME/data/cnn_dm/finished_files/chunked/train_* --vocab_path=$HOME/data/cnn_dm/finished_files/vocab --log_root=$HOME/working_dir/cnn_dm/RLSeq2Seq/ --exp_name=actor-critic-ddqn --batch_size=80 --max_iter=20000
Adding Critic model to the current model

We can use Dueling network to train the DDQN by activating dueling_net flag. Moreover, we can choose to update the target network using polyak averaging by dqn_polyak_averaging flag.

CUDA_VISIBLE_DEVICES=0,1 python src/ --mode=train --data_path=$HOME/data/cnn_dm/finished_files/chunked/train_* --vocab_path=$HOME/data/cnn_dm/finished_files/vocab --log_root=$HOME/working_dir/cnn_dm/RLSeq2Seq/ --exp_name=actor-critic-ddqn --batch_size=80 --max_iter=21000 --ac_training=True --dueling_net=True --dqn_polyak_averaging=True --convert_to_reinforce_model=True --dqn_gpu_num=1
Start Pre-Training Critic with fixed Actor

Use dqn_pretrain_steps flag to set how many iteration you want to pre-train the Critic.

CUDA_VISIBLE_DEVICES=0,1 python src/ --mode=train --data_path=$HOME/data/cnn_dm/finished_files/chunked/train_* --vocab_path=$HOME/data/cnn_dm/finished_files/vocab --log_root=$HOME/working_dir/cnn_dm/RLSeq2Seq/ --exp_name=actor-critic-ddqn --batch_size=80 --ac_training=True --dqn_pretrain=True --dueling_net=True --dqn_polyak_averaging=True --dqn_gpu_num=1
Start Training Actor/Critic using True Q-Estimates

We can run Actor in one GPU and Critic in another GPU simply by using a different GPU number for Critic using dqn_gpu_num option. Also as mentioned before, we should avoid using true Q-estimation for long, therefore, we use true estimation to train DDQN for only 1000 iterations.

CUDA_VISIBLE_DEVICES=0,1 python src/ --mode=train --data_path=$HOME/data/cnn_dm/finished_files/chunked/train_* --vocab_path=$HOME/data/cnn_dm/finished_files/vocab --log_root=$HOME/working_dir/cnn_dm/RLSeq2Seq/ --exp_name=actor-critic-ddqn --batch_size=80 --max_iter=22000 --ac_training=True --dueling_net=True --dqn_polyak_averaging=True --calculate_true_q=True --dqn_gpu_num=1
Start Training Actor/Critic using Q-Estimates

Please note that we don't use calculate_true_q flag, anymore.

CUDA_VISIBLE_DEVICES=0,1 python src/ --mode=train --data_path=$HOME/data/cnn_dm/finished_files/chunked/train_* --vocab_path=$HOME/data/cnn_dm/finished_files/vocab --log_root=$HOME/working_dir/cnn_dm/RLSeq2Seq/ --exp_name=actor-critic-ddqn --batch_size=80 --max_iter=40000 --ac_training=True --dueling_net=True --dqn_polyak_averaging=True --dqn_gpu_num=1
Decoding based on Actor and Critic estimation
CUDA_VISIBLE_DEVICES=0 python src/ --mode=decode --data_path=$HOME/data/cnn_dm/finished_files/chunked/test_* --vocab_path=$HOME/data/cnn_dm/finished_files/vocab --log_root=$HOME/working_dir/cnn_dm/RLSeq2Seq/ --exp_name=actor-critic-ddqn --ac_training=True --dueling_net=True --dqn_polyak_averaging=True --dqn_gpu_num=1 --single_pass=1 --beam_size=4

Please note that we can use options such as intradecoder, temporal_attention, E2EBackProp, scheduled_sampling, etc in Actor-Critic models, too. Using these options will help to have a better performing Actor model.

Under Development

Supporting YAML configs

Storing ReplayBuffer in disk for Actor-Critic models


Thanks @astorfi for his help on preparing this documentation.