1,720,959 research outputs found
Correlation minimizing replay memory in temporal-difference reinforcement learning
Full text linkOnline reinforcement learning agents are now able to process an increasing amount of data which makes their approximation and compression into value functions a more demanding task. To improve approximation, thus the learning process itself, it has been proposed to select randomly a mini-batch of the past experiences that are stored in the replay memory buffer to be replayed at each learning step. In this work, we present an algorithm that classifies and samples the experiences into separate contextual memory buffers using an unsupervised learning technique. This allows each new experience to be associated to a mini-batch of the past experiences that are not from the same contextual buffer as the current one, thus further reducing the correlation between experiences. Experimental results show that the correlation minimizing sampling improves over Q-learning algorithms with uniform sampling, and that a significant improvement can be observed when coupled with the sampling methods that prioritize on the experience temporal difference error
Towards learning agents with personality traits: Modeling Openness to Experience
Full text linkRecent advances in neurosciences and cognitive sciences show us that the human neocortex is not a slave to the experiences from our perception and that the memories stored in hippocampus are goal weighted during the replay of the experiences for the purpose of relearning from them. Temporal difference reinforcement learning systems that use neural networks as function approximators rely on an experience replay memory structure similar to the hippocampus. We bring forward this similarity and present a novel way of using a goal weighted prioritization of the memory that is biologically inspired. Furthermore, we introduce a novel prioritization criteria called Variety of Experience Index, or VEI, for weighting the selection of the experiences that are stored in the replay memory. Weighting the experiences based on two different extremes of VEI can behaviourally modify the agent's learning process, generating different types of learning agents that exhibit different personality traits along the dimension of Openness to Experience. (C) 2019 Elsevier B.V. All rights reserved
Adaptation of learning agents through artificial perception
Full text linkThe process of online reinforcement learning also creates a stream of experiences that an agent can store to re-learn from them. In this work, we introduce a concept of artificial perception affecting the dynamics of experience memory replay, which induces a secondary goal-directed drive that complements the main goal defined by the reinforcement function. The different perception dynamics are capable of inducing different "personality" types able to govern the agent behavior, possibly enabling it to exhibit an improved performance over an environment with specific characteristics. Experimental results show that different personalities show different performance levels when facing environment variations, therefore, showcasing the influence of artificial perception in agent's adaptation
Augmented Memory Replay in Reinforcement Learning With Continuous Control
Full text linkOnline reinforcement learning agents are currently able to process an increasing amount of data by converting it into a higher order value functions. This expansion of the information collected from the environment increases the agent’s state space enabling it to scale up to more complex problems but also increases the risk of forgetting by learning on redundant or conflicting data. To improve the approximation of a large amount of data, a random mini-batch of the past experiences that are stored in the replay memory buffer is often replayed at each learning step. The proposed work takes inspiration from a biological mechanism which acts as a protective layer of higher cognitive functions found in mammalian brain: active memory consolidation mitigates the effect of forgetting previous memories by dynamically processing the new ones. Similar dynamics are implemented by the proposed augmented memory replay or AMR algorithm. The architecture of AMR, based on a simple artificial neural network is able to provide an augmentation policy which modifies each of the agents experiences by augmenting their relevance prior to storing them in the replay memory. The function approximator of AMR is evolved using genetic algorithm in order to obtain the specific augmentation policy function that yields the best performance of a learning agent in a specific environment given by its received cumulative reward. Experimental results show that an evolved AMR augmentation function capable of increasing the significance of the specific memories is able to further increase the stability and convergence speed of the learning algorithms dealing with the complexity of continuous action domains
Uncertainty maximization in partially observable domains: A cognitive perspective
Full text linkFaced with an ever-increasing complexity of their domains of application, artificial learning agents are now able to scale up in their ability to process an overwhelming amount of data. However, this comes at the cost of encoding and processing an increasing amount of redundant information. This work exploits the possibility of learning systems, applied in partially observable domains, to selectively focus on the specific type of information that is more likely related to the causal interaction among transitioning states. A temporal difference displacement criterion is defined to implement adaptive masking of the observations. It can enable a significant improvement of convergence of temporal difference algorithms applied to partially observable Markov processes, as shown by experiments performed under a variety of machine learning problems, ranging from highly complex visuals as Atari games to simple textbook control problems such as CartPole. The proposed framework can be added to most RL algorithms since it only affects the observation process, selecting the parts more promising to explain the dynamics of the environment and reducing the dimension of the observation space
Informed Sampling of Prioritized Experience Replay
No full textExperience replay plays an essential role as an information-generating mechanism in reinforcement learning systems that use neural networks as function approximators. It enables the artificial learning agents to store their past experiences in a sliding-window buffer, effectively recycling them in the process of a continual re-training of a neural network. The intermediary process of experience caching opens a possibility for an agent to optimize the order in which the experiences are sampled from the buffer. This may improve the default standard, i.e., the stochastic prioritization based on Temporal-Difference error (or TD-error), which focuses on experiences that carry more Temporal-Difference surprise for the approximator. A notion of informed prioritization is proposed, a method relying on fast on-line confidence estimates of approximator predictions in order to be able to dynamically exploit the benefits of TD-error prioritization only when its prediction confidence about the selected experiences increases. The presented informed-stochastic prioritization method of replay buffer sampling, implemented as a part of standard staple Deep Q-learning algorithm outperformed the vanilla stochastic prioritization based on TD-error in 41 out of 54 trialed Atari games
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Full text linkWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
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