Breakthroughs, discoveries, and DIY tips sent every weekday. It's something out of a dream or TV show: a married couple takes their dog for a walk and finds a buried treasure worth $10 million. But it actually happened, back in 2013. The treasure is the Saddle Ridge Hoard, the largest ever stash of gold coins found in the United States. The couple, who go by John and Mary in the press, have been careful to obscure their identity and the exact place where they live to prevent would-be treasure hunters from showing up on their property.

The town was important to the Celtic Mediomatrici tribe before it was conquered by Julius Caesar. Breakthroughs, discoveries, and DIY tips sent every weekday. Archeologists recently discovered over 40,000 Roman-era coins during a dig in a French village. The treasure trove of ancient coins were found in three ceramic storage vessels that had been buried between 1,700 and 1,800 years ago. The team from the National Institute for Preventive Archaeological Research (INRAP) was digging in the village of Senon in northeastern France, roughly 60 miles from the Luxembourg border.

The Great Canadian Treasure Hunt's first clue is a 13-stanza poem. Breakthroughs, discoveries, and DIY tips sent every weekday. An actual treasure chest filled with around $1 million in gold coins is hidden somewhere in Canada. However, the mystery isn't tied to a centuries' old pirate bounty or unsolved bank heist, however. These riches were instead intentionally hidden by a mining consortium to celebrate the country's "rich mining heritage and spirit of adventure."

Breakthroughs, discoveries, and DIY tips sent every weekday. You can now own a 1 gold coin celebrating one of America's most revolutionary achievements: the NASA Space Shuttle program. The latest variant in the ongoing American Innovation 1 Coin series is available to order through the United States Mint. Selected to represent the state of Florida, the noncirculating legal tender is the third coin released this year and the 28th coin in the 15-year project first announced in 2018. While the coin's front displays the series' Statue of Liberty image, the back shows the shuttle launching above plumes of exhaust.

The chain-of-though (CoT) prompting methods were successful in various natural language processing (NLP) tasks thanks to their ability to unveil the underlying complex reasoning processes. Such reasoning processes typically exhibit implicitly structured steps. Recent efforts also started investigating methods to encourage more explicitly structured reasoning procedures to be captured. In this work, we propose Tab-CoT, a novel tabular-format CoT prompting method, which allows the complex reasoning process to be explicitly modelled in a highly structured manner. Despite its simplicity, we show that our approach is capable of performing reasoning across multiple dimensions (i.e., both rows and columns). We demonstrate our approach's strong zero-shot and few-shot capabilities through extensive experiments on a range of reasoning tasks.

Diffusion models have demonstrated their powerful generative capability in many tasks, with great potential to serve as a paradigm for offline reinforcement learning. However, the quality of the diffusion model is limited by the insufficient diversity of training data, which hinders the performance of planning and the generalizability to new tasks. This paper introduces AdaptDiffuser, an evolutionary planning method with diffusion that can self-evolve to improve the diffusion model hence a better planner, not only for seen tasks but can also adapt to unseen tasks. AdaptDiffuser enables the generation of rich synthetic expert data for goal-conditioned tasks using guidance from reward gradients. It then selects high-quality data via a discriminator to finetune the diffusion model, which improves the generalization ability to unseen tasks. Empirical experiments on two benchmark environments and two carefully designed unseen tasks in KUKA industrial robot arm and Maze2D environments demonstrate the effectiveness of AdaptDiffuser. For example, AdaptDiffuser not only outperforms the previous art Diffuser by 20.8% on Maze2D and 7.5% on MuJoCo locomotion, but also adapts better to new tasks, e.g., KUKA pick-and-place, by 27.9% without requiring additional expert data. More visualization results and demo videos could be found on our project page.

This is the first in a series of articles on reinforcement learning and OpenAI Gym. Suppose you're playing a video game. You enter a room with two doors. Behind Door 1 are 100 gold coins, followed by a passageway. Behind Door 2 is 1 gold coin, followed by a second passageway going in a different direction. Once you go through one of the doors, there is no going back.