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The game of chess is the most widely-studied domain in the history of artificial intelligence.
Ranked #1 on Game of Go on ELO Ratings
OpenSpiel is a collection of environments and algorithms for research in general reinforcement learning and search/planning in games.
Developing visual perception models for active agents and sensorimotor control are cumbersome to be done in the physical world, as existing algorithms are too slow to efficiently learn in real-time and robots are fragile and costly.
In this work we aim to solve this problem by optimizing the efficiency and resource utilization of reinforcement learning algorithms instead of relying on distributed computation.
Harnessing the transformer's ability to process long time horizons of information could provide a similar performance boost in partially observable reinforcement learning (RL) domains, but the large-scale transformers used in NLP have yet to be successfully applied to the RL setting.
The universal Bayesian agent AIXI (Hutter, 2005) is a model of a maximally intelligent agent, and plays a central role in the sub-field of general reinforcement learning (GRL).
We have added to the GRL simulation platform AIXIjs the functionality to assign an agent arbitrary discount functions, and an environment which can be used to determine the effect of discounting on an agent's policy.
In the Arcade Learning Environment (ALE), small changes in environment parameters such as stochasticity or the maximum allowed play time can lead to very different performance.
Many dynamic processes, including common scenarios in robotic control and reinforcement learning (RL), involve a set of interacting subprocesses.