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SMARTS: Scalable Multi-Agent Reinforcement Learning Training School for Autonomous Driving
TLDR
The design goals of SMARTS (Scalable Multi-Agent RL Training School) are described, its basic architecture and its key features are explained, and its use is illustrated through concrete multi-agent experiments on interactive scenarios.
Sparse Spiking Gradient Descent
TLDR
This work presents the first sparse SNN backpropagation algorithm which achieves the same or better accuracy as current state of the art methods while being significantly faster and more memory efficient.
Modelling Behavioural Diversity for Learning in Open-Ended Games
TLDR
By incorporating the diversity metric into best-response dynamics, this work develops diverse fictitious play and diverse policy-space response oracle for solving normalform games and open-ended games and proves the uniqueness of the diverse best response and the convergence of the algorithms on two-player games.
Online Double Oracle
TLDR
This paper proposes new learning algorithms for solving two-player zero-sum normal-form games where the number of pure strategies is prohibitively large and ODO is rationale in the sense that each agent in ODO can exploit strategic adversary with a regret bound of O.
Learning to Shape Rewards using a Game of Switching Controls
TLDR
It is proved that ROSA, which easily adopts existing RL algorithms, learns to construct a shapingreward function that is tailored to the task thus ensuring efficient convergence to high performance policies.
LIGS: Learnable Intrinsic-Reward Generation Selection for Multi-Agent Learning
TLDR
A new general framework for improving coordination and performance of multi-agent reinforcement learners (MARL), named Learnable Intrinsic-Reward Generation Selection algorithm (LIGS), which introduces an adaptive learner, Generator that observes the agents and learns to construct intrinsic rewards online that coordinate the agents’ joint exploration and joint behaviour.