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Found 9 papers, 5 papers with code
Mixtures of Experts Unlock Parameter Scaling for Deep RL
The recent rapid progress in (self) supervised learning models is in large part predicted by empirical scaling laws: a model's performance scales proportionally to its size.
Analysing the Sample Complexity of Opponent Shaping
Providing theoretical guarantees for M-FOS is hard because A) there is little literature on theoretical sample complexity bounds for meta-reinforcement learning B) M-FOS operates in continuous state and action spaces, so theoretical analysis is challenging.
Scaling Opponent Shaping to High Dimensional Games
In multi-agent settings with mixed incentives, methods developed for zero-sum games have been shown to lead to detrimental outcomes.
Leading the Pack: N-player Opponent Shaping
We evaluate on over 4 different environments, varying the number of players from 3 to 5, and demonstrate that model-based OS methods converge to equilibrium with better global welfare than naive learning.
JaxMARL: Multi-Agent RL Environments in JAX
This not only enables GPU acceleration, but also provides a more flexible MARL environment, unlocking the potential for self-play, meta-learning, and other future applications in MARL.
Adversarial Cheap Talk
More specifically, we show that an ACT Adversary is capable of harming performance by interfering with the learner's function approximation, or instead helping the Victim's performance by outputting useful features.
Model-Free Opponent Shaping
In general-sum games, the interaction of self-interested learning agents commonly leads to collectively worst-case outcomes, such as defect-defect in the iterated prisoner's dilemma (IPD).
COLA: Consistent Learning with Opponent-Learning Awareness
Finally, in an empirical evaluation on a set of general-sum games, we find that COLA finds prosocial solutions and that it converges under a wider range of learning rates than HOLA and LOLA.
Recurrent Neural Processes
We extend Neural Processes (NPs) to sequential data through Recurrent NPs or RNPs, a family of conditional state space models.
