Predicting distant future trajectories of agents in a dynamic scene is not an easy problem because the future trajectory of an agent is affected by not only his her past trajectory but also the scene contexts. To tackle this problem, we propose a model based on recurrent neural networks (RNNs) and a novel method for training the model. The proposed model is based on an encoder-decoder architecture where the encoder encodes inputs (past trajectories and scene context information) while the decoder produces a trajectory from the context vector given by the encoder. We train the networks of the proposed model to produce a future trajectory, which is the closest to the true trajectory, while maximizing a reward from a reward function. The reward function is also trained at the same time to maximize the margin between the rewards from the ground-truth trajectory and its estimate. The reward function plays the role of a regularizer for the proposed model so the trained networks are able to better utilize the scene context information for the prediction task. We evaluated the proposed model on several public datasets. Experimental results show that the prediction performance of the proposed model is much improved by the regularization, which outperforms the-state-of-the-arts in terms of accuracy.
Most widely used techniques for regularizing RNN are drop-out @cite_10 and @math 2-regularization @cite_13 on the trainable parameters of the networks. Recently, @cite_2 proposed an RNN regularization technique taking into account caption generation task. They introduced an additional RNN, called @math (ARNet), which encourages the current hidden state vector of the decoder RNN to embed more information from the previous one. Introducing a discriminator network to generate multiple realistic future trajectories @cite_34 @cite_26 can be considered as the regularization on RNN. In this paper, we propose a regularization method based on inverse reinforcement learning framework. The proposed regularization approach differs from @cite_34 @cite_26 @cite_28 in the following points. First, only the encoder RNN is regularized by a reward function so that the encoder RNN learns how to efficiently encode meaningful features extracted from inputs. Second, the reward function evaluates if the predicted positions are realistic along with the scene context information caused by the positions. In contrast, the discriminator networks in @cite_34 @cite_26 use only the past and predicted future trajectories for the evaluation.
@cite_26 @cite_28 @cite_10 @cite_2 @cite_34 @cite_13
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This paper proposes a general method for improving the structure and quality of sequences generated by a recurrent neural network (RNN), while maintaining information originally learned from data, as well as sample diversity. An RNN is first pre-trained on data using maximum likelihood estimation (MLE), and the probability distribution over the next token in the sequence learned by this model is treated as a prior policy. Another RNN is then trained using reinforcement learning (RL) to generate higher-quality outputs that account for domain-specific incentives while retaining proximity to the prior policy of the MLE RNN. To formalize this objective, we derive novel off-policy RL methods for RNNs from KL-control. The effectiveness of the approach is demonstrated on two applications; 1) generating novel musical melodies, and 2) computational molecular generation. For both problems, we show that the proposed method improves the desired properties and structure of the generated sequences, while maintaining information learned from data. This paper proposes a general method for improving the structure and quality of sequences generated by a recurrent neural network (RNN), while maintaining information originally learned from data, as well as sample diversity. An RNN is first pre-trained on data using maximum likelihood estimation (MLE), and the probability distribution over the next token in the sequence learned by this model is treated as a prior policy. Another RNN is then trained using reinforcement learning (RL) to generate higher-quality outputs that account for domain-specific incentives while retaining proximity to the prior policy of the MLE RNN. To formalize this objective, we derive novel off-policy RL methods for RNNs from KL-control. The effectiveness of the approach is demonstrated on two applications; 1) generating novel musical melodies, and 2) computational molecular generation. For both problems, we show that the proposed method improves the desired properties and structure of the generated sequences, while maintaining information learned from data. In this paper, we investigate the use of recurrent neural networks (RNNs) in the context of search-based online advertising. We use RNNs to map both queries and ads to real valued vectors, with which the relevance of a given (query, ad) pair can be easily computed. On top of the RNN, we propose a novel attention network, which learns to assign attention scores to different word locations according to their intent importance (hence the name DeepIntent). The vector output of a sequence is thus computed by a weighted sum of the hidden states of the RNN at each word according their attention scores. We perform end-to-end training of both the RNN and attention network under the guidance of user click logs, which are sampled from a commercial search engine. We show that in most cases the attention network improves the quality of learned vector representations, evaluated by AUC on a manually labeled dataset. Moreover, we highlight the effectiveness of the learned attention scores from two aspects: query rewriting and a modified BM25 metric. We show that using the learned attention scores, one is able to produce sub-queries that are of better qualities than those of the state-of-the-art methods. Also, by modifying the term frequency with the attention scores in a standard BM25 formula, one is able to improve its performance evaluated by AUC. In the first part of this paper, a regular recurrent neural network (RNN) is extended to a bidirectional recurrent neural network (BRNN). The BRNN can be trained without the limitation of using input information just up to a preset future frame. This is accomplished by training it simultaneously in positive and negative time direction. Structure and training procedure of the proposed network are explained. In regression and classification experiments on artificial data, the proposed structure gives better results than other approaches. For real data, classification experiments for phonemes from the TIMIT database show the same tendency. In the second part of this paper, it is shown how the proposed bidirectional structure can be easily modified to allow efficient estimation of the conditional posterior probability of complete symbol sequences without making any explicit assumption about the shape of the distribution. For this part, experiments on real data are reported.