Surviving High Uncertainty in Logistics with MARL
Part 2. Building scale-invariant agents that seamlessly change contexts The post Surviving High Uncertainty in Logistics with MARL appeared first on Towards Data Science .
Explore the latest AI news and research tagged #reinforcement learning — curated from top sources including OpenAI, Anthropic, Google DeepMind, and more.
Part 2. Building scale-invariant agents that seamlessly change contexts The post Surviving High Uncertainty in Logistics with MARL appeared first on Towards Data Science .
Solving multiplayer games with function approximation The post Playing Connect Four with Deep Q-Learning appeared first on Towards Data Science .
Multi-tool-integrated reasoning enables LLM-empowered tool-use agents to solve complex tasks by interleaving natural-language reasoning with calls to external tools. However, training such agents using outcome-only rewards suffers from credit-assignment ambiguity,…
In this post, we take a deeper look at how RLAIF or RL with LLM-as-a-judge works with Amazon Nova models effectively.
The vendor’s goal is achieving superintelligence.
Ineffable Intelligence, a British AI lab founded a mere few months ago by former DeepMind researcher David Silver, has raised $1.1 billion in funding at a valuation of $5.1 billion.
Learn about function approximation and the different choices for approximation functions The post Introduction to Approximate Solution Methods for Reinforcement Learning appeared first on Towards Data Science .
GRASP is a new gradient-based planner for learned dynamics (a “world model”) that makes long-horizon planning practical by (1) lifting the trajectory into virtual states so optimization is parallel across…
In this post, I’m trying to put forward a narrow, pedagogical point, one that comes up mainly when I’m arguing in favor of LLMs having limitations that human learning does…
In this post, I’ll introduce a reinforcement learning (RL) algorithm based on an “alternative” paradigm: divide and conquer . Unlike traditional methods, this algorithm is not based on temporal difference…
Training Diffusion Models with Reinforcement Learning We deployed 100 reinforcement learning (RL)-controlled cars into rush-hour highway traffic to smooth congestion and reduce fuel consumption for everyone. Our goal is to…
Is Attention all you need? Mamba, a novel AI model based on State Space Models (SSMs), emerges as a formidable alternative to the widely used Transformer models, addressing their inefficiency…
With diverse environments, we can analyze, diagnose and edit deep reinforcement learning models using attribution.
A closer look at how Temporal Difference Learning merges paths of experience for greater statistical efficiency