It was not long ago that the world watched World Chess Champion Garry Kasparov lose a decisive match against a supercomputer. IBM's Deep Blue embodied the state of the art in the late 1990s, when a machine defeating a world (human) champion at a complex game such as chess was still unheard of. Fast-forward to today, and not only have supercomputers greatly surpassed Deep Blue in chess, they have managed to achieve superhuman performance in a string of other games, often much more complex than chess, ranging from Go to Dota to classic Atari titles. Many of these games have been mastered just in the last five years, pointing to a pace of innovation much quicker than the two decades prior. Recently, Google released work on Agent57, which for the first time showcased superior performance over existing benchmarks across all 57 Atari 2600 games. The class of AI algorithms underlying these feats -- deep-reinforcement learning -- has demonstrated the ability to learn at very high levels in constrained domains, such as the ones offered by games.

Reinforcement learning has seen a great deal of success in solving complex decision making problems ranging from robotics to games to supply chain management to recommender systems. Despite their success, deep reinforcement learning algorithms can be exceptionally difficult to use, due to unstable training, sensitivity to hyperparameters, and generally unpredictable and poorly understood convergence properties. Multiple explanations, and corresponding solutions, have been proposed for improving the stability of such methods, and we have seen good progress over the last few years on these algorithms. In this blog post, we will dive deep into analyzing a central and underexplored reason behind some of the problems with the class of deep RL algorithms based on dynamic programming, which encompass the popular DQN and soft actor-critic (SAC) algorithms – the detrimental connection between data distributions and learned models. Before diving deep into a description of this problem, let us quickly recap some of the main concepts in dynamic programming.

This paper proposes a detailed and extensive comparison of the Trust Region Policy Optimization and DeepQ-Network with Normalized Advantage Functions with respect to other state of the art algorithms, namely Deep Deterministic Policy Gradient and Vanilla Policy Gradient. Comparisons demonstrate that the former have better performances then the latter when asking robotic arms to accomplish manipulation tasks such as reaching a random target pose and pick &placing an object. Both simulated and real-world experiments are provided. Simulation lets us show the procedures that we adopted to precisely estimate the algorithms hyper-parameters and to correctly design good policies. Real-world experiments let show that our polices, if correctly trained on simulation, can be transferred and executed in a real environment with almost no changes.

An important goal in reinforcement learning is to create agents that can quickly adapt to new goals while avoiding situations that might cause damage to themselves or their environments. One way agents learn is through exploration mechanisms, which are needed to discover new policies. However, in deep reinforcement learning, exploration is normally done by injecting noise in the action space. While performing well in many domains, this setup has the inherent risk that the noisy actions performed by the agent lead to unsafe states in the environment. Here we introduce a novel approach called Meta-Learned Instinctual Networks (MLIN) that allows agents to safely learn during their lifetime while avoiding potentially hazardous states. At the core of the approach is a plastic network trained through reinforcement learning and an evolved "instinctual" network, which does not change during the agent's lifetime but can modulate the noisy output of the plastic network. We test our idea on a simple 2D navigation task with no-go zones, in which the agent has to learn to approach new targets during deployment. MLIN outperforms standard meta-trained networks and allows agents to learn to navigate to new targets without colliding with any of the no-go zones. These results suggest that meta-learning augmented with an instinctual network is a promising new approach for safe AI, which may enable progress in this area on a variety of different domains.

Income inequality is one of the overarching problems of economics. One of the most effective tools policymakers have to address it is taxation: governments collect money from people according to what they earn and redistribute it either directly, via welfare schemes, or indirectly, by using it to pay for public projects. But though more taxation can lead to greater equality, taxing people too much can discourage them from working or motivate them to find ways to avoid paying--which reduces the overall pot. Getting the balance right is not easy. Economists typically rely on assumptions that are hard to validate.

INFO 7375 - Special Topics in Artificial Intelligence Engineering and Applications - Computational Skepticism is looking for experts to speak online this summer on a variety of subjects. The Computational Skepticism class is starting today!!! I'd like to thank Kinesso, H2O.ai, Squark Ai, ArrowDx, and the Computational Radiology Laboratory at Harvard/BCH for expressing an interest in speaking with the class. These are all online talks and can be with just a small group of around 20, or we can invite the thousands of Masters students in MGENs Boston, Silicon Valley, and Seattle campuses. These subjects include data quality and completeness, bias and fairness, AutoML, model interpretability, causal inference, counterfactual models, deep learning pipeline (AutoDL), time-series pipeline (AutoTS), feature engineering pipeline (AutoFE), autoVisualization (AutoViz), reinforcement learning pipeline (AutoRL), evidence knowledge graphs (EKG) We are looking for more companies and research groups that may be willing to share data and present how they are using machine learning.

Neural networks are effective function approximators, but hard to train in the reinforcement learning (RL) context mainly because samples are correlated. For years, scholars have got around this by employing experience replay or an asynchronous parallel-agent system. This paper proposes Discrete-to-Deep Supervised Policy Learning (D2D-SPL) for training neural networks in RL. D2D-SPL discretises the continuous state space into discrete states and uses actor-critic to learn a policy. It then selects from each discrete state an input value and the action with the highest numerical preference as an input/target pair. Finally it uses input/target pairs from all discrete states to train a classifier. D2D-SPL uses a single agent, needs no experience replay and learns much faster than state-of-the-art methods. We test our method with two RL environments, the Cartpole and an aircraft manoeuvring simulator.

We consider the problem of demand-side energy management, where each household is equipped with a smart meter that is able to schedule home appliances online. The goal is to minimise the overall cost under a real-time pricing scheme. While previous works have introduced centralised approaches, we formulate the smart grid environment as a Markov game, where each household is a decentralised agent, and the grid operator produces a price signal that adapts to the energy demand. The main challenge addressed in our approach is partial observability and perceived non-stationarity of the environment from the viewpoint of each agent. We propose a multi-agent extension of a deep actor-critic algorithm that shows success in learning in this environment. This algorithm learns a centralised critic that coordinates training of all agents. Our approach thus uses centralised learning but decentralised execution. Simulation results show that our online deep reinforcement learning method can reduce both the peak-to-average ratio of total energy consumed and the cost of electricity for all households based purely on instantaneous observations and a price signal.

Classical Planning is concerned with finding plans, or sequences of actions, that when applied to some initial condition specified by a set of logical predicates, will bring the environment to a state that satisfies a set of goal predicates. This is usually performed by some heuristic search procedure, and the resulting plan is applicable only to the specific instance that was solved. However, a possibly stronger outcome would be to find some sort of higher level plan that can solve many instances that belong to the same domain, and thus share an underlying structure. The study of methods that can discover such higher level plans is called Generalized Planning. Generalized plans do not necessarily exist for all classical planning domains, but finding such solutions for domains in which it is possible could obviate the need to perform compute intensive search in cases where we only wish to find a goal satisfying solution. To give an example of such a generalized plan, let us consider a simplified Blocksworld domain. In this domain there are unique blocks that can be either stacked on each other or strewn about the floor, and the goal is to stack and unstack blocks such that we arrive at a goal configuration from an initial configuration. Finding a plan that does so in an optimal number of steps is generally NPhard [10], but finding a plan that satisfies the goal regardless of cost can be done in polynomial time in the following manner: 1. Unstack all the blocks so that they are scattered on the floor 2. stack the block according to the goal configuration, beginning with the lower blocks This strategy is not optimal since we might unstack blocks that are already in their proper place according to the goal specification, but it will yield a goal satisfying plan for every instance in this simplified Blocksworld domain.

In this paper, we study a joint detection, mapping and navigation problem for a single unmanned aerial vehicle (UAV) equipped with a low complexity radar and flying in an unknown environment. The goal is to optimize its trajectory with the purpose of maximizing the mapping accuracy and, at the same time, to avoid areas where measurements might not be sufficiently informative from the perspective of a target detection. This problem is formulated as a Markov decision process (MDP) where the UAV is an agent that runs either a state estimator for target detection and for environment mapping, and a reinforcement learning (RL) algorithm to infer its own policy of navigation (i.e., the control law). Numerical results show the feasibility of the proposed idea, highlighting the UAV's capability of autonomously exploring areas with high probability of target detection while reconstructing the surrounding environment.