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Found 5 papers, 0 papers with code
Multi-Agent Learning of Efficient Fulfilment and Routing Strategies in E-Commerce
This paper presents an integrated algorithmic framework for minimising product delivery costs in e-commerce (known as the cost-to-serve or C2S).
Using General Value Functions to Learn Domain-Backed Inventory Management Policies
We consider the inventory management problem, where the goal is to balance conflicting objectives such as availability and wastage of a large range of products in a store.
Follow your Nose: Using General Value Functions for Directed Exploration in Reinforcement Learning
Exploration methods have been used to sample better trajectories in large environments while auxiliary tasks have been incorporated where the reward is sparse.
School of hard knocks: Curriculum analysis for Pommerman with a fixed computational budget
In this paper, we focus on developing a curriculum for learning a robust and promising policy in a constrained computational budget of 100, 000 games, starting from a fixed base policy (which is itself trained to imitate a noisy expert policy).
Accelerating Training in Pommerman with Imitation and Reinforcement Learning
Our methodology involves training an agent initially through imitation learning on a noisy expert policy, followed by a proximal-policy optimization (PPO) reinforcement learning algorithm.
