This paper proposes an original approach based on expert trajectories combination and Deep Reinforcement Learning to provide a better MineCraft player. The combination is based on the idea that the problem is naturally decomposable and the search space presents large plateaus. We use two steps approach to build a better trajectory from all existed expert trajectories and consequently to extract an optimal policy. The first step uses Birch clustering approach and images cosine similarity to obtain compact representation and substantial state and action space reduction. To reduce the overall complexity, the image distances are computed in images latent space trained by an encoder-decoder model. In the second step, we first eliminate plateaus to keep only the nodes with non-zero rewards then we compare trajectories using the Bellman equation and an appropriate value function. By checking the incremental compatibility of the trajectory of compact representations, we build the solution combining the best compatible sub-trajectories of the experts. The experimental results on NeurIPS MineRL 2020 challenge show that training the actors model on the most rewarding extracted subset of trajectories leads to achieve state-of-the-art performances on the MineCraft environment. The paper's source code is available here: https://github.com/thomJeffDoe/CompareTrajectories.

Mining association rules is an important technique for discovering meaningful patterns in datasets. Temporal association rule mining can be decomposed into two phases: finding temporal frequent patterns and finding temporal rules construction. Till date, a large number of algorithms have been proposed in the area of mining association rules. However, most of these algorithms consider patterns as a collection of point primitives and their three basic relations (). Several applications consider patterns with duration and need to reason about intervals and their thirteen possible relationships. In this paper we investigate properties of temporal sequences represented as a collection of intervals. We present a simple framework for temporal sequence and describe DATTES (Discovering pATterns in TEmporal Sequences), an innovative algorithm using interval properties to mine temporal patterns. The framework can be used to mine temporal association rules. According to some interval algebra properties, this paper introduces a new confidence evaluation function for mining temporal rules. Experiments on real dataset (human face identification problem) show the effectiveness and the performances of this approach.

Sequential pattern mining allows to discover temporal relationship between items within a database. The patterns can then be used to generate association rules. When the databases are very large, the execution speed and the memory usage of the mining algorithm become critical parameters. Previous research has focused on either one of the two parameters. In this paper, we present bitSPADE, a novel algorithm that combines the best features of SPAM, one of the fastest algorithm, and SPADE, one of the most memory efficient algorithm. Moreover, we introduce a new pruning strategy that enables bitSPADE to reach high performances. Experimental evaluations showed that bitSPADE ensures an efficient tradeoff between speed and memory usage by outperforming SPADE by both speed and memory usage factors more than 3.4 and SPAM by a memory consumption factor up to more than an order of magnitude.