Deep reinforcement learning (DRL) recently has attained remarkable results in various domains, including games, robotics, and recommender system. Nevertheless, an urgent problem in the practical application of DRL is fast adaptation. To this end, this article proposes a new and versatile metalearning approach called fast task adaptation via metalearning (FTAML), which leverages the strengths of the model-based methods and gradient-based metalearning methods for training the initial parameters of the model, such that the model is able to efficiently master unseen tasks with a little amount of data from the tasks. The proposed algorithm makes it possible to separate task optimization and task identification, specifically, the model-based learner helps to identify the pattern of a task, while the gradient-based metalearner is capable of consistently improving the performance with only a few gradient update steps through making use of the task embedding produced by the model-based learner. In addition, the choice of network for the model-based learner in the proposed method is also discussed, and the performance of networks with different depths is explored. Finally, the simulation results on reinforcement learning problems demonstrate that the proposed approach outperforms compared metalearning algorithms and delivers a new state-of-the-art performance on a variety of challenging control tasks.

The popular deepQlearning algorithm is known to be instability because of theQ-value’s shake and overestimation action values under certain conditions. These issues tend to adversely affect their performance. In this paper, we develop the ensemble network architecture for deep reinforcement learning which is based on value function approximation. The temporal ensemble stabilizes the training process by reducing the variance of target approximation error and the ensemble of target values reduces the overestimate and makes better performance by estimating more accurateQ-value. Our results show that this architecture leads to statistically significant better value evaluation and more stable and better performance on several classical control tasks at OpenAI Gym environment.

Most of the existing knowledge representation learning methods project the entities and relations represented by symbols in the knowledge graph into the low-dimensional vector space from the perspective of the structure and semantics of triples, and express the complex relations between entities and relations with dense low-dimensional vectors. However, triples in the knowledge graph not only contain relation triples, but also contain a large number of attribute triples. Existing knowledge representation methods often confuse these two kinds of triples and pay little attention to the semantic information contained in attributes and attribute values. In this paper, a novel representation learning method which makes use of the attribute information of entities is proposed. Specifically, deep convolutional neural network model is used to encode attribute information of entities, and both attribute information and triple structure information are utilized to learn knowledge representation, and then generate attribute-based representation of entities. The knowledge graph completion task was used to evaluate this method, and the experimental results on open data sets FB15K and FB24k showed that the attribute-embodied knowledge representation learning model outperforms the other baselines.

multi-agent deep reinforcement learning (MDRL) is an emerging research hotspot and application direction in the field of machine learning and artificial intelligence. MDRL covers many algorithms, rules and frameworks, it is currently researched in swarm system, energy allocation optimization, stocking analysis, sequential social dilemma, and with extremely bright future. In this paper, a parallel-critic method based on classic MDRL algorithm MADDPG is proposed to alleviate the training instability problem in cooperative-competitive multi-agent environment. Furthermore, a policy smoothing technique is introduced to our proposed method to decrease the variance of learning policies. The suggested method is evaluated in three different scenarios of authoritative multi-agent particle environment (MPE). Multiple statistical data of experimental results show that our method significantly improves the training stability and performance compared to vanilla MADDPG.

Recently, the application of deep reinforcement learning in the recommender system is flourishing and stands out by overcoming drawbacks of traditional methods and achieving high recommendation quality. The dynamics, long-term returns, and sparse data issues in the recommender system have been effectively solved. But the application of deep reinforcement learning brings problems of interpretability, overfitting, complex reward function design, and user cold start. This study proposed a tag-aware recommender system based on deep reinforcement learning without complex function design, taking advantage of tags to make up for the interpretability problems existing in the recommender system. Our experiment is carried out on the MovieLens dataset. The result shows that the DRL-based recommender system is superior than traditional algorithms in minimum error, and the application of tags have little effect on accuracy when making up for interpretability. In addition, the DRL-based recommender system has excellent performance on user cold start problems.

The assumption of IRL is that demonstrations are optimally acting in an environment. In the past, most of the work on IRL needed to calculate optimal policies for different reward functions. However, this requirement is difficult to satisfy in large or continuous state space tasks. Let alone continuous action space. We propose a continuous maximum entropy deep inverse reinforcement learning algorithm for continuous state space and continues action space, which realizes the depth cognition of the environment model by the way of reconstructing the reward function based on the demonstrations, and a hot start mechanism based on demonstrations to make the training process faster and better. We compare this new approach to well‐known IRL algorithms using Maximum Entropy IRL, DDPG, hot start DDPG, etc. Empirical results on classical control environments on OpenAI Gym: MountainCarContinues‐v0 show that our approach is able to learn policies faster and better.