Deep reinforcement learning has the potential to address various scientific problems. In this paper, we implement an optics simulation environment for reinforcement learning based controllers. The environment captures the essence of nonconvexity, nonlinearity, and time-dependent noise inherent in optical systems, offering a more realistic setting. Subsequently, we provide the benchmark results of several reinforcement learning algorithms on the proposed simulation environment. The experimental findings demonstrate the superiority of off-policy reinforcement learning approaches over traditional control algorithms in navigating the intricacies of complex optical control environments. The code of the paper is available at https://github.com/Walleclipse/Reinforcement-Learning-Pulse-Stacking.

In this paper, we discuss a Reinforcement Learning (RL) approach towards a class of sequential decision problems, exemplified for the popular Wordle puzzle that appears daily in the New York Times. Wordle involves a list of 5-letter mystery words, which is a subset of a larger list of guess words. A word is selected at random from the mystery list, and the objective is to find that word by sequentially selecting no more than six words from the guess list. Each guess word selection provides information about the letters contained in the hidden mystery word according to a given set of rules, which involves color coding of letters shared by the guess word and the mystery word. We will adopt a more general point of view, by considering a broad class of problems that include Wordle as a special case. In particular, the problems that we consider include sequential search situations, where the objective is to guess correctly an unknown object from a given finite set of objects (the set of mystery words in the Wordle context), by using a sequence of decisions from a finite set (the set of guess words in Wordle), which result in a sequence of corresponding observations (the information outcomes of the guesses in Wordle). We aim to minimize some cost function, such as the expected number of observations required to determine the unknown object. Within the search context just described, some basic information theory concepts are relevant, which have already been applied to Wordle, and are important for our methodology.

Most mathematical questions one could have about Wordle are settled by now, and a few remain open. I summarize here what is known, as far as I can tell. First, let's clarify a few things about the game: Wordle comes with a dictionary of 12972 words that the player is allowed to use as guesses. They are essentially all 5-letter combinations one could reasonably argue are English words. The "secret" word that the player has to discover is also always in that dictionary.

Communication lays the foundation for human cooperation. It is also crucial for multi-agent cooperation. However, existing work focuses on broadcast communication, which is not only impractical but also leads to information redundancy that could even impair the learning process. To tackle these difficulties, we propose \textit{Individually Inferred Communication} (I2C), a simple yet effective model to enable agents to learn a prior for agent-agent communication. The prior knowledge is learned via causal inference and realized by a feed-forward neural network that maps the agent's local observation to a belief about who to communicate with. The influence of one agent on another is inferred via the joint action-value function in multi-agent reinforcement learning and quantified to label the necessity of agent-agent communication. Furthermore, the agent policy is regularized to better exploit communicated messages. Empirically, we show that I2C can not only reduce communication overhead but also improve the performance in a variety of multi-agent cooperative scenarios, comparing to existing methods.

Sparse reward is one of the most challenging problems in reinforcement learning (RL). Hindsight Experience Replay (HER) attempts to address this issue by converting a failed experience to a successful one by relabeling the goals. Despite its effectiveness, HER has limited applicability because it lacks a compact and universal goal representation. We present Augmenting experienCe via TeacheR's adviCE (ACTRCE), an efficient reinforcement learning technique that extends the HER framework using natural language as the goal representation. We first analyze the differences among goal representation, and show that ACTRCE can efficiently solve difficult reinforcement learning problems in challenging 3D navigation tasks, whereas HER with non-language goal representation failed to learn. We also show that with language goal representations, the agent can generalize to unseen instructions, and even generalize to instructions with unseen lexicons. We further demonstrate it is crucial to use hindsight advice to solve challenging tasks, and even small amount of advice is sufficient for the agent to achieve good performance.

Trial of the Sword: By accessing this location, players can challenge the new Trial of the Sword (previously known as "Cave of Trials Challenge"), where enemies appear one after another. Link starts without any armor or weapons, and if he defeats all of the enemies in the room he can proceed to the next area. Trial of the Sword will include around 45 total rooms for players to complete. When Link clears all of the trials, the true power of the Master Sword will awaken and always be in its glowing powered-up state. Hard Mode: The Legend of Zelda: Breath of the Wild is already considered one of the most thrilling games in The Legend of Zelda series, and fans looking for a challenge are in for a treat with the new Hard Mode.

For some time now, my colleague Dave Thier and I have argued back and forth about the merits of an'easy mode' for notoriously challenging games like Dark Souls. So the debate continues with Nioh, Team Ninja's excellent new Samurai action-RPG which draws a lot of inspiration from such infamously challenging games as Dark Souls and Ninja Gaiden. Dave's argument essentially boils down to this: Nioh's difficulty will turn off more casual players or players who don't have the kind of time to sink into such a challenging game. This is both foolish (as it limits sales) and snobby (as it leaves the game accessible only to the hardcore audience) and would be easily mitigated by the inclusion of an easy mode, making it more accessible to all while leaving the core experience unscathed. Every game we play sets out to do something different.