Deep Reinforcement Learning in Match-3 Game
An increasing number of algorithms in deep reinforcement learning area creates new challenges for environments, particularly, for their comprehensive analysis and searching application areas. The key purpose of this article is to provide an extensible environment for researches. We consider a Match-3 game, which has simple gameplay, but challenging game design for engaging players. The article provides metrics for evaluation of agents and corresponding baselines in different scenarios.
In this work, we study deep reinforcement algorithms for partially observable Markov decision processes (POMDP) combined with Deep Q-Networks. To our knowledge, we are the first to apply standard Markov decision process architectures to POMDP scenarios. We propose an extension of DQN with Dueling Networks and several other model-free policies to training agent using deep reinforcement learning in VizDoom environment, which is replication of Doom first-person shooter. We develop several agents for the following scenarios in VizDoom first-person shooter (FPS): Basic, Defend The Center, Health Gathering. We compare our agent with Recurrent DQN with Prioritized Experience Replay and Snapshot Ensembling agent and get approximately triple increase in per episode reward. It is important to say that POMDP scenario close the gap between human and computer player scenarios thus providing more meaningful justification for Deep RL agent performance.
In this work, we study the effect of combining existent improvements for Deep Q-Networks (DQN) in Markov Decision Processes (MDP) and Partially Observable MDP (POMDP) settings. Combinations of several heuristics, such as Distributional Learning and Dueling architectures improvements, for MDP are well-studied. We propose a new combination method of simple DQN extensions and develop a new model-free reinforcement learning agent, which works with POMDP and uses well-studied improvements from fully observable MDP. To test our agent we choose the VizDoom environment, which is old first person shooter, and the Health Gathering scenario. We prove that improvements used in MDP setting may be used in POMDP setting as well and our combined agents can converge to better policies. We develop an agent with combination of several improvements showing superior game performance in practice. We compare our agent with Recurrent DQN using Prioritized Experience Replay and Snaphot Ensembling agent and get approximately triple increase in per episode reward.
The seminal model by Laurent Itti and Cristoph Koch demonstrated that we can compute the entire flow of visual processing from input to resulting fixations. Despite many replications and follow-ups, few have matched the impact of the original model - so what made this model so groundbreaking? We have selected five key contributions that distinguish the original salience model by Itti and Koch; namely its contribution to our theoretical, neural and computational understanding of visual processing. Further, the model showed how salience could be used to make predictions for both spatial and temporal distributions of fixations. During the last 20 years, advances in the field have brought up various techniques and approaches to salience modeling, many of which tried to augment the initial Itti and Koch model. One of the most recent trends has been to adopt the computational power of deep learning neural networks, however, this has also shifted their primary focus to spatial classification. We present a review of recent approaches to modeling salience, and discuss the models from the point of view of their contribution to computational cognitive neuroscience.
his volume presents the results of the Neural Information Processing Systems Competition track at the 2018 NeurIPS conference. The competition follows the same format as the 2017 competition track for NIPS. Out of 21 submitted proposals, eight competition proposals were selected, spanning the area of Robotics, Health, Computer Vision, Natural Language Processing, Systems and Physics.
Competitions have become an integral part of advancing state-of-the-art in artificial intelligence (AI). They exhibit one important difference to benchmarks: Competitions test a system end-to-end rather than evaluating only a single component; they assess the practicability of an algorithmic solution in addition to assessing feasibility.
We consider deep reinforcement learning algorithms for playing a game based on video input. We compare choosing proper hyper-parameters in deep Q-network model and model-free episodic control focused on reusing of successful strategies. The evaluation was made based on Pong video game implemented in Unreal Engine 4.
The 4th Workshop on Representation Learning for NLP (RepL4NLP) will be hosted by ACL 2019 and held on 2 August 2019. The workshop is being organised by Isabelle Augenstein, Spandana Gella, Sebastian Ruder, Katharina Kann, Burcu Can, Alexis Conneau, Johannes Welbl, Xian Ren and Marek Rei; and advised by Kyunghyun Cho, Edward Grefenstette, Karl Moritz Hermann, Chris Dyer and Laura Rimell. The workshop is organised by the ACL Special Interest Group on Representation Learning (SIGREP) and receives generous sponsorship from Facebook AI Research, Amazon, and Naver.
The 4th Workshop on Representation Learning for NLP aims to continue the success of the 1st Workshop on Representation Learning for NLP (about 50 submissions and over 250 attendees; second most attended collocated event at ACL’16 after WMT), 2nd Workshop on Representation Learning for NLP and 3rd Workshop on Representation Learning for NLP. The workshop was introduced as a synthesis of several years of independent *CL workshops focusing on vector space models of meaning, compositionality, and the application of deep neural networks and spectral methods to NLP. It provides a forum for discussing recent advances on these topics, as well as future research directions in linguistically motivated vector-based models in NLP.
Applications that cater to the needs of disaster incident response generate large amount of data and demand large computational resource access. Such datasets are usually collected in real-time at the incident scenes using different Internet of Things (IoT) devices. Hierarchical clouds, i.e., core and edge clouds, can help these applications’ real-time data orchestration challenges as well as with their IoT operations scalability, reliability and stability by overcoming infrastructure limitations at the ad-hoc wireless network edge. Routing is a crucial infrastructure management orchestration mechanism for such systems. Current geographic routing or greedy forwarding approaches designed for early wireless ad-hoc networks lack efficient solutions for disaster incident-supporting applications, given the high-speed and low-latency data delivery that edge cloud gateways impose. In this paper, we present a novel Artificial Intelligent (AI)-augmented geographic routing approach, that relies on an area knowledge obtained from the satellite imagery (available at the edge cloud) by applying deep learning. In particular, we propose a stateless greedy forwarding that uses such an environment learning to proactively avoid the local minimum problem by diverting traffic with an algorithm that emulates electrostatic repulsive forces. In our theoretical analysis, we show that our Greedy Forwarding achieves in the worst case a path stretch approximation bound with respect to the shortest path, without assuming presence of symmetrical links or unit disk graphs. We evaluate our approach with both numerical and event-driven simulations, and we establish the practicality of our approach in a real incident-supporting hierarchical cloud deployment to demonstrate improvement of application level throughput due to a reduced path stretch under severe node failures and high mobility challenges of disaster response scenarios.