While this is not close to the state-of-the-art in general (c.f. Figure 2 shows the effect of action entropy on exploratory behavior in Montezuma's Revenge. Number of unique avatar positions visited. Full training curves across all 6 Atari games are shown in Figure 1, including the random policy baseline. To ensure this didn't hamper performance, we At each state visited by the agent evaluator during training, the agent's state (consisting of the avatar's The full curves are included for completeness. The compute cluster we performed experiments on is heterogenous, and has features such as host-sharing, adaptive load-balancing, etc.

Learning how the world works is central to building AI agents that can adapt to complex environments. Traditional world models based on deep learning demand vast amounts of training data, and do not flexibly update their knowledge from sparse observations. Recent advances in program synthesis using Large Language Models (LLMs) give an alternate approach which learns world models represented as source code, supporting strong generalization from little data. To date, application of program-structured world models remains limited to natural language and grid-world domains. We introduce a novel program synthesis method for effectively modeling complex, non-gridworld domains by representing a world model as an exponentially-weighted product of programmatic experts (PoE-World) synthesized by LLMs. We show that this approach can learn complex, stochastic world models from just a few observations. We evaluate the learned world models by embedding them in a model-based planning agent, demonstrating efficient performance and generalization to unseen levels on Atari's Pong and Montezuma's Revenge. We release our code and display the learned world models and videos of the agent's gameplay at https://topwasu.github.io/poe-world.

FPS has much higher success rate than uniform sampling: 0.4 vs 0.15 at

We'll also mention that the loss

A new video game sparked fury and accusations of wokeness in entertainment. But we've played this game before--and it's boring. Back in the summer of 2020, during the first year of COVID lockdowns, two first-party PlayStation games were released back-to-back, just a month apart: and . Upon release, was pretty beloved by a specific right-wing culture-war gamer crowd, who placed it on a pedestal specifically as a way to directly attack . While is far from perfect (for example, Neil Druckmann, the game's creator and co-director, took inspiration from the Israel-Palestine conflict that was criticized for both-sidesism), but the game's sin on release for many on the political right was that it took a series whose lead was previously a man and continued its story with one lead who was a lesbian and another whose appearance was deemed too masculine for these players to be attracted to her.

At the (tail) end of 2024, Billie Eilish sat cross-legged on stage and began to miaow. Her fans erupted in harmony, each belting out an off-key miaow of their own. This is because Eilish's Oscar-winning track What Was I Made For? – a lachrymose Barbie cut lamenting adulthood's entailing ennui – has become the default soundtrack for a new breed of cat video. You may recognise it: the song often plays over the top of these AI-generated fantasias featuring a cartoonishly fat cat or an equally buff feline with a suspiciously veiny human body. The cat cheats on her lover, falls pregnant or seeks revenge in a weirdly condensed soap opera.

LLMs are rapidly evolving from fascinating research projects into indispensable tools for myriad text-based tasks, including generation, note-taking, and complex writing assignments. This ascent is marked by significant strides in two key areas: a notable improvement in their factual accuracy, and a marked reduction in the propensity for "hallucination"--the generation of plausible but false or nonsensical information. Simultaneously, the very definition of an LLM's output is expanding, moving beyond text to embrace a rich tapestry of sound, imagery, and even video. These advancements are solidifying LLMs' position as increasingly reliable and versatile partners in creative and analytical endeavors. One hurdle limiting the widespread adoption of LLMs has been the concern over the veracity of their outputs.