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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 .
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,…
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’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…
Kwai AI's SRPO framework slashes LLM RL post-training steps by 90% while matching DeepSeek-R1 performance in math and code. This two-stage RL approach with history resampling overcomes GRPO limitations. The…
DeepSeek AI, a prominent player in the large language model arena, has recently published a research paper detailing a new technique aimed at enhancing the scalability of general reward models…
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…
With diverse environments, we can analyze, diagnose and edit deep reinforcement learning models using attribution.