#reinforcement learning — AI News & Research · DeepTrendLab

Playing Connect Four with Deep Q-Learning

Solving multiplayer games with function approximation The post Playing Connect Four with Deep Q-Learning appeared first on Towards Data Science .

PORTool: Importance-Aware Policy Optimization with Rewarded Tree for Multi-Tool-Integrated Reasoning

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,…

Reinforcement fine-tuning with LLM-as-a-judge

In this post, we take a deeper look at how RLAIF or RL with LLM-as-a-judge works with Amazon Nova models effectively.

Record $1.1B Seed Funding for Reinforcement Learning Startup

The vendor’s goal is achieving superintelligence.

DeepMind’s David Silver just raised $1.1B to build an AI that learns without human data

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.

Introduction to Approximate Solution Methods for Reinforcement Learning

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 .

Gradient-based Planning for World Models at Longer Horizons

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…

You can’t imitation-learn how to continual-learn

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…

Keep the Tokens Flowing: Lessons from 16 Open-Source RL Libraries

RL without TD learning

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…

Scaling Up Reinforcement Learning for Traffic Smoothing: A 100-AV Highway Deployment

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…

Mamba Explained

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…

Understanding RL Vision

With diverse environments, we can analyze, diagnose and edit deep reinforcement learning models using attribution.

The Paths Perspective on Value Learning

A closer look at how Temporal Difference Learning merges paths of experience for greater statistical efficiency