Reinforcement Learning
MunchausenReinforcementLearning
The resulting agent outperforms RainbowonAtari, installing anewState oftheArtwith very little modifications to the original algorithm.
Asynchronous Actor-Critic for Multi-Agent Reinforcement Learning
Temporally-extended actions have been widely used in both learning and planning to improve scalability and reduce complexity.
TowardtheFundamentalLimitsofImitation Learning
We then propose a novel algorithm based on minimum-distance functionals in the setting where the transition model is given and the expert is deterministic.Thealgorithmissuboptimalby .|S|H3/2/N,matchingourlower
Overleaf Example
We model episode sessions--parts of the episode where the latent state isfixed--and propose three keymodifications toexisting meta-RL methods: (i) consistency of latent information within sessions, (ii) session masking, and (iii) priorlatent conditioning.
