Sydney Thomas dominates the red carpet in Cannes as her star continues to rise, new MLB power couple & MEAT! Viral staff photo reveals just how bloated Stephen Colbert's'Late Show' operation really was Four of the most controversial television finales in honor of'The Boys' despised ending Sophie Cuningham has heads spinning with her pregame outfit, Colbert's final jab & lessons from Kyle Busch Adrenaline-packed preview released for upcoming D-Day film'Pressure,' features loaded cast Kacey Musgraves responds to'fat activist' furious because she can't fit into her new Walmart clothing line Selena Gomez is reportedly bringing her talents to award-winning director's new four-hour X-rated movie Minka Kelly uncorks a heater at 45, ABS backfires spectacularly and LSU parents vs a security guard! Robot's lifeless corpse hauled off stage after fall during disastrous Michael Jackson impression Bear cubs spar on woman's front porch in adorable viral nature video, reactions pour in President Trump says deal with Iran is'largely negotiated' Democrats' 2024 election autopsy avoids key issues, panelists say Kiron Skinner warns NATO alliance undergoing'major restructuring' amid global conflicts OutKick-Culture Man finds poop on his roof, and if that wasn't bad enough, it led to a mountain lion encounter The man said he'took off running' after the big cat appeared ready to pounce near his guest house pool area Jorts are the newest fashion trend... It's never a good day when you find poop on your roof . No one is going to go, Hey, there's some dookie on my roof, I think I'll go buy some lottery tickets because clearly, everything is going my way. Bill Dabney of Pasadena was one of the unlucky few who had roof turds on his guest house that led directly to a big cat encounter.

We characterise the phenomenon empirically, verifying that it is not limited to specific algorithm or environment properties.

In this work we continue to build upon recent advances in reinforcement learning for finite Markov processes. A common approach among previous existing algorithms, both single-actor and distributed, is to either clip rewards or to apply a transformation method on Q-functions to handle a large variety of magnitudes in real discounted returns. We theoretically show that one of the most successful methods may not yield an optimal policy if we have a non-deterministic process. As a solution, we argue that distributional reinforcement learning lends itself to remedy this situation completely. By the introduction of a conjugated distributional operator we may handle a large class of transformations for real returns with guaranteed theoretical convergence. We propose an approximating single-actor algorithm based on this operator that trains agents directly on unaltered rewards using a proper distributional metric given by the Cram\'er distance. To evaluate its performance in a stochastic setting we train agents on a suite of 55 Atari 2600 games using sticky-actions and obtain state-of-the-art performance compared to other well-known algorithms in the Dopamine framework.

Developments in artificial intelligence often draw inspiration from how humans think, but now AI has turned the tables to teach us about how brains learn. Will Dabney at tech firm DeepMind in London and his colleagues have found that a recent development in machine learning called distributional reinforcement learning also provides a new explanation for how the reward pathways in the brain work. These pathways govern our response to pleasurable events and are mediated by neurons that release the brain chemical dopamine. "Dopamine in the brain is a type of surprise signal," says Dabney. "When things turn out better than expected, more dopamine gets released."

Being Optimistic to Be Conservative: Quickly Learning a CV aR Policy Ramtin Keramati 1, Christoph Dann 2, Alex T amkin 3, Emma Brunskill 3 1 Institute of Computational and Mathematical Engineering (ICME), Stanford University, California, USA 2 Machine Learning Department, Carnegie Mellon University, Pittsburgh, Pennsylvania, USA 3 Department of Computer Science, Stanford University, California, USA {keramati,atamkin,ebrun } @cs.stanford.edu Abstract While maximizing expected return is the goal in most reinforcement learning approaches, risk-sensitive objectives such as conditional value at risk (CV aR) are more suitable for many high-stakes applications. However, relatively little is known about how to explore to quickly learn policies with good CV aR. In this paper, we present the first algorithm for sample-efficient learning of CV aR-optimal policies in Markov decision processes based on the optimism in the face of uncertainty principle. This method relies on a novel optimistic version of the distributional Bellman operator that moves probability mass from the lower to the upper tail of the return distribution. We prove asymptotic convergence and optimism of this operator for the tabular policy evaluation case. We further demonstrate that our algorithm finds CV aR-optimal policies substantially faster than existing baselines in several simulated environments with discrete and continuous state spaces. Introduction A key goal in reinforcement learning (RL) is to quickly learn to make good decisions by interacting with an environment. In most cases the quality of the decision policy is evaluated with respect to its expected (discounted) sum of rewards. However, in many interesting cases, it is important to consider the full distributions over the potential sum of rewards, and the desired objective may be a risk-sensitive measure of this distribution. For example, a patient undergoing a surgery for a knee replacement will (hopefully) only experience that procedure once or twice, and may will be interested in the distribution of potential results for a single procedure, rather than what may happen on average if he or she were to undertake that procedure hundreds of time. Finance and (machine) control are other cases where interest in risk-sensitive outcomes are common. A popular risk-sensitive measure of a distribution of outcomes is the Conditional V alue at Risk (CV aR) (Artzner et al. 1999). Intuitively, CV aR is the expected reward in the worst α -fraction of outcomes, and has seen extensive use in financial portfolio optimization (Zhu and Fukushima 2009), often under the name "expected shortfall".

Since their introduction a year ago, distributional approaches to reinforcement learning (distributional RL) have produced strong results relative to the standard approach which models expected values (expected RL). However, aside from convergence guarantees, there have been few theoretical results investigating the reasons behind the improvements distributional RL provides. In this paper we begin the investigation into this fundamental question by analyzing the differences in the tabular, linear approximation, and non-linear approximation settings. We prove that in many realizations of the tabular and linear approximation settings, distributional RL behaves exactly the same as expected RL. In cases where the two methods behave differently, distributional RL can in fact hurt performance when it does not induce identical behaviour. We then continue with an empirical analysis comparing distributional and expected RL methods in control settings with non-linear approximators to tease apart where the improvements from distributional RL methods are coming from.

"Ted came up with the breakthrough idea that got rid of the computer so you didn't have to have a computer to make the game work," Allan Alcorn, one of Atari's first employees, said in an interview this week. " It created the industry." Samuel Frederick Dabney, Jr., was born in San Francisco on May 2, 1937. His parents, Irma and Samuel Frederick Dabney, divorced when he was young, and he was raised by his father, an accountant. A brother, Doug, died in 2013.

Al Alcorn knew he was being wooed. Nolan Bushnell, the tall, brash, young engineer from Alcorn's work-study days at Ampex, had shown up at Alcorn's Sunnyvale office. Bushnell was driving a new blue station wagon. "It's a company car," he said with feigned nonchalance. He offered to drive Alcorn, recently hired as an associate engineer at Ampex, to see the "game on a TV screen" that Bushnell and Ted Dabney had developed at their new startup company. The two men drove to an office in Mountain View, near the highway. The space was large, about 10,000 square feet, and looked like a cross between an electronics lab and an assembly warehouse. Oscilloscopes and lab benches filled one area. Half-built cabinets and screen with wires protruding from them sat in another. Bushnell walked with Alcorn to a sinuous, six-foot-tall fiberglass cabinet with a screen at eye level. Bushnell was proud of what he called its "spacey-looking" shape.