Browsing: reinforcement

Moonshot AI’s Kimi team and kvcache-ai have open-sourced AgentENV (AENV), a distributed platform for running agent environments at scale. AgentENV powers agentic reinforcement learning (RL) training…

Most reinforcement learning benchmarks reset the world after every episode. Real operations never reset. Skyfall AI’s MORPHEUS targets that gap. It is a persistent enterprise simulation…

Most search agents are trained as policies over a growing transcript. The model decides how to search. It must also remember what it saw, which evidence…

is often introduced through a long list of algorithms. SARSA, Q-learning, PPO, DQN, SAC etc. Each name seems to point to a different method, a different…

@dataclass class MemoryItem: memory_id: int topic: str entity: str slot: str value: str text: str def build_memory_bank() -> List[MemoryItem]: entities = [ { “entity”: “Astra”, “topic”:…

series about Reinforcement Learning (RL), following Sutton and Barto’s famous book “Reinforcement Learning” [1]. In the previous posts we finished dissecting Part I of said book,…

, Reinforcement Learning — learning from observations and rewards — is the method most alike to the way humans (and animals) learn.  Despite this similarity, it also remains the most…

In the current landscape of generative AI, the ‘scaling laws’ have generally dictated that more parameters equal more intelligence. However, Liquid AI is challenging this convention…

NVIDIA researchers introduced ProRL AGENT, a scalable infrastructure designed for reinforcement learning (RL) training of multi-turn LLM agents. By adopting a ‘Rollout-as-a-Service’ philosophy, the system decouples…

In this tutorial, we implement a reinforcement learning agent using RLax, a research-oriented library developed by Google DeepMind for building reinforcement learning algorithms with JAX. We…