An integral function of fully autonomous robots and humans is the ability to focus attention on a few relevant percepts to reach a certain goal while disregarding irrelevant percepts. Humans and animals rely on the interactions between the Pre-Frontal Cortex (PFC) and the Basal Ganglia (BG) to achieve this focus called Working Memory (WM). The Working Memory Toolkit (WMtk) was developed based on a computational neuroscience model of this phenomenon with Temporal Difference (TD) Learning for autonomous systems. Recent adaptations of the toolkit either utilize Abstract Task Representations (ATRs) to solve Non-Observable (NO) tasks or storage of past input features to solve Partially-Observable (PO) tasks, but not both. We propose a new model, PONOWMtk, which combines both approaches, ATRs and input storage, with a static or dynamic number of ATRs. The results of our experiments show that PONOWMtk performs effectively for tasks that exhibit PO, NO, or both properties.

Free Coupon Discount - Artificial Intelligence: Reinforcement Learning in Python, Complete guide to Artificial Intelligence, prep for Deep Reinforcement Learning with Stock Trading Applications Created by Lazy Programmer Inc. Students also bought Data Science: Deep Learning in Python Recommender Systems and Deep Learning in Python PyTorch: Deep Learning and Artificial Intelligence Advanced AI: Deep Reinforcement Learning in Python Deep Learning Prerequisites: Logistic Regression in Python Preview this Udemy Course GET COUPON CODE Description When people talk about artificial intelligence, they usually don't mean supervised and unsupervised machine learning. These tasks are pretty trivial compared to what we think of AIs doing - playing chess and Go, driving cars, and beating video games at a superhuman level. Reinforcement learning has recently become popular for doing all of that and more. Much like deep learning, a lot of the theory was discovered in the 70s and 80s but it hasn't been until recently that we've been able to observe first hand the amazing results that are possible. In 2016 we saw Google's AlphaGo beat the world Champion in Go.

One of the goals of AI research is to teach machines how to do the same things people do, but better. In the early 2000s, this meant focusing on problems like flying helicopters and walking up flights of stairs. However, there's still a massive list of problems where humans outperform machines. Although we can no longer claim to beat machines at tasks like Go and image classification, we have a distinct advantage in solving problems that aren't as well-defined, like judging a well-executed backflip, cleaning a room while preventing accidents, and perhaps the most human problem of all: reasoning about people's values. Since all these tasks contain some degree of subjectivity, machines need information about the world as well as a way to learn about the people within it in order to solve these problems.

"Value functions are a core component of [RL] systems. The main idea is to to construct a single function approximator V(s; θ) that estimates the long-term reward from any state s, using parameters θ. In this paper we introduce universal value function approximators (UVFAs) V(s, g; θ) that generalise not just over states s but also over goals g." Here is a rigorous, mathematical formulation of RL that treats goals (the high-level objective of the skill to be learned, which should yield good rewards) as a fundamental and necessary input rather than something to be discovered from just the reward signal. The agent is told what it's supposed to do, just as is done in zero-shot learning and actual human learning. It has been 3 years since this was published, and how many papers have cited it since?

This paper investigates the geometrical properties of real world games (e.g. Tic-Tac-Toe, Go, StarCraft II). We hypothesise that their geometrical structure resemble a spinning top, with the upright axis representing transitive strength, and the radial axis, which corresponds to the number of cycles that exist at a particular transitive strength, representing the non-transitive dimension. We prove the existence of this geometry for a wide class of real world games, exposing their temporal nature. Additionally, we show that this unique structure also has consequences for learning - it clarifies why populations of strategies are necessary for training of agents, and how population size relates to the structure of the game. Finally, we empirically validate these claims by using a selection of nine real world two-player zero-sum symmetric games, showing 1) the spinning top structure is revealed and can be easily re-constructed by using a new method of Nash clustering to measure the interaction between transitive and cyclical strategy behaviour, and 2) the effect that population size has on the convergence in these games.

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Collaborative multi-agent reinforcement learning is an important sub-field of the multiagent reinforcement learning (MARL), where the agents learn to coordinate to achieve joint success. It has wide applications in traffic control [Kuyer et al., 2008], autonomous driving [Shalev-Shwartz et al., 2016] and smart grid [Yang et al., 2018]. To learn a coordination, the interactions between agents are indispensable. For instance, humans can reason about other's behaviors or know other peoples' intentions through communication and then determine an effective coordination plan. However, how to design a mechanism of such interaction in a principled way and at the same time solve the large scale real-world applications is still a challenging problem. Recently, there is a surge of interest in solving the collaborative MARL problem [Foerster et al., 2018, Qu et al., 2019, Lowe et al., 2017]. Among them, joint policy approaches have demonstrated their superiority [Rashid et al., 2018, Sunehag et al., 2018, Oliehoek et al., 2016]. A straightforward approach is to replace the action in the single-agent reinforcement learning by the joint action a (a 1, a 2,..., a N), while it obviously suffers from the issue of the exponentially large action space.

Combining model-based and model-free learning systems has been shown to improve the sample efficiency of learning to perform complex robotic tasks. However, dual-system approaches fail to consider the reliability of the learned model when it is applied to make multiple-step predictions, resulting in a compounding of prediction errors and performance degradation. In this paper, we present a novel dual-system motor learning approach where a meta-controller arbitrates online between model-based and model-free decisions based on an estimate of the local reliability of the learned model. The reliability estimate is used in computing an intrinsic feedback signal, encouraging actions that lead to data that improves the model. Our approach also integrates arbitration with imagination where a learned latent-space model generates imagined experiences, based on its local reliability, to be used as additional training data. We evaluate our approach against baseline and state-of-the-art methods on learning vision-based robotic grasping in simulation and real world. The results show that our approach outperforms the compared methods and learns near-optimal grasping policies in dense- and sparse-reward environments.

In a new study published this week on the preprint server Arxiv.org, Both it and several baseline models will soon be available on GitHub. One of the most powerful techniques in machine learning -- reinforcement learning, which entails spurring software agents toward goals via rewards -- is also one of the most flawed. It's sample inefficient, meaning it requires a large number of compute cycles to complete, and without additional data to cover variations, it adapts poorly to environments that differ from the training environment. It's theorized that prior knowledge of tasks through structured language could be combined with reinforcement learning to mitigate its shortcomings, and BabyAI was designed to put this theory to the test.

In real-world multi-robot systems, performing high-quality, collaborative behaviors requires robots to asynchronously reason about high-level action selection at varying time durations. Macro-Action Decentralized Partially Observable Markov Decision Processes (MacDec-POMDPs) provide a general framework for asynchronous decision making under uncertainty in fully cooperative multi-agent tasks. However, multi-agent deep reinforcement learning methods have only been developed for (synchronous) primitive-action problems. This paper proposes two Deep Q-Network (DQN) based methods for learning decentralized and centralized macro-action-value functions with novel macro-action trajectory replay buffers introduced for each case. Evaluations on benchmark problems and a larger domain demonstrate the advantage of learning with macro-actions over primitive-actions and the scalability of our approaches.