Evolution of cooperation and competition can appear when multiple adaptive agents share a biological, social, or technological niche. In the present work we study how emerge between autonomous that learn by reinforcement while using only their raw visual input as state representation. particular, extend Deep Q-Learning framework to multiagent environments investigate interaction two learning in well-known video game Pong. By manipulating classical rewarding scheme Pong show competitive...

Recent advances in the field of artificial intelligence have revealed principles about neural processing, particular vision. Previous work demonstrated a direct correspondence between hierarchy human visual areas and layers deep convolutional networks (DCNN) trained on object recognition. We use DCNN to investigate which frequency bands correlate with feature transformations increasing complexity along ventral pathway. By capitalizing intracranial depth recordings from 100 patients we assess...

Multiagent systems appear in most social, economical, and political situations. In the present work we extend Deep Q-Learning Network architecture proposed by Google DeepMind to multiagent environments investigate how two agents controlled independent Q-Networks interact classic videogame Pong. By manipulating classical rewarding scheme of Pong demonstrate competitive collaborative behaviors emerge. Competitive learn play score efficiently. Agents trained under schemes find an optimal...

Model precision in a classification task is highly dependent on the feature space that used to train model. Moreover, whether features are sequential or static will dictate which method can be applied as most of machine learning algorithms designed deal with either one another type data. In real-life scenarios, however, it often case both and dynamic present, extracted from this work, we demonstrate how generative models such Hidden Markov Models (HMM) Long Short-Term Memory (LSTM)...

Reinforcement Learning (RL) algorithms typically require millions of environment interactions to learn successful policies in sparse reward settings. Hindsight Experience Replay (HER) was introduced as a technique increase sample efficiency by reimagining unsuccessful trajectories ones altering the originally intended goals. However, it cannot be directly applied visual environments where goal states are often characterized presence distinct features. In this work, we show how can...

Success stories of applied machine learning can be traced back to the datasets and environments that were put forward as challenges for community. The challenge community sets a benchmark is usually eventually solves. ultimate reinforcement research train real agents operate in environment, but until now there has not been common real-world RL benchmark. In this work, we present prototype environment from OffWorld Gym -- collection robotics with free public remote access. Close integration...

Numerous studies in the area of BCI are focused on search for a better experimental paradigm-a set mental actions that user can evoke consistently and machine discriminate reliably. Examples such activities motor imagery, computations, etc. We propose technique instead allows to try different ones will work best.The system is based modification self-organizing map (SOM) algorithm enables interactive communication between learning through visualization user's state space. During interaction...

Previous work demonstrated a direct correspondence between the hierarchy of human visual areas and layers deep convolutional neural networks (DCNN) trained on object recognition. We used DCNNs to investigate which frequency bands correlate with feature transformations increasing complexity along ventral pathway. By capitalizing intracranial depth recordings from 100 patients 11293 electrodes we assessed alignment DCNN signals at different in time windows. found that gamma activity,...

In this work, a classification method for SSVEP-based BCI is proposed. The uses features extracted by traditional methods and finds optimal discrimination thresholds each feature to classify the targets. Optimising formalised as maximisation task of performance measure BCIs called information transfer rate (ITR). However, instead standard calculating ITR, which makes certain assumptions about data, more general formula derived avoid incorrect ITR calculation when are not met. This allows be...

Human brain has developed mechanisms to efficiently decode sensory information according perceptual categories of high prevalence in the environment, such as faces, symbols, objects. Neural activity produced within localized networks been associated with process that integrates both bottom-up and cognitive top-down processing. Yet, how specifically different types components neural responses reflect local networks' selectivity for categorical processing is still unknown. In this work we...

Human brain has developed mechanisms to efficiently decode sensory information according perceptual categories of high prevalence in the environment, such as faces, symbols, objects. Neural activity produced within localized networks been associated with process that integrates both bottom-up and cognitive top-down processing. Yet, how specifically different types components neural responses reflect local networks' selectivity for categorical processing is still unknown. By mimicking...

Abstract Objective Numerous studies in the area of BCI are focused on search for a better experimental paradigm – set mental actions that user can evoke consistently and machine discriminate reliably. Examples such activities motor imagery, computations, etc. We propose technique instead allows to try different ones will work best. Approach The system is based modification self-organizing map (SOM) algorithm enables interactive communication between learning through visualization user’s...

The thesis explores the role machine learning methods play in creating intuitive computational models of neural processing. Combined with interpretability techniques, could replace human modeler and shift focus effort to extracting knowledge from ready-made articulating that into descroptions reality. This perspective makes case favor larger exploratory data-driven approach neuroscience while coexisting alongside traditional hypothesis-driven approach. We exemplify proposed context...

Recent advances in batch (offline) reinforcement learning have shown promising results from available offline data and proved to be an essential toolkit control policies a model-free setting. An algorithm applied dataset collected by suboptimal non-learning-based can result policy that outperforms the behavior agent used collect data. Such scenario is frequent robotics, where existing automation collecting operational Although techniques learn generated sub-optimal agent, there still...