Standard Markov Decision Process (MDP) formulations of RL and simulated environments mirroring the MDP structure assume secure access to feedback (e. g., rewards).
How can we design agents that pursue a given objective when all feedback mechanisms are influenceable by the agent?
To avoid this interference incentive, we introduce a baseline policy that represents a default course of action (such as doing nothing), and use it to filter out future tasks that are not achievable by default.
Can humans get arbitrarily capable reinforcement learning (RL) agents to do their bidding?
Proposals for safe AGI systems are typically made at the level of frameworks, specifying how the components of the proposed system should be trained and interact with each other.
How can we design safe reinforcement learning agents that avoid unnecessary disruptions to their environment?
We present a suite of reinforcement learning environments illustrating various safety properties of intelligent agents.
Traditional RL methods fare poorly in CRMDPs, even under strong simplifying assumptions and when trying to compensate for the possibly corrupt rewards.
This paper presents a new memory-bounded left-corner parsing model for unsupervised raw-text syntax induction, using unsupervised hierarchical hidden Markov models (UHHMM).