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Found 6 papers, 1 papers with code
Reinforcement Learning from Bagged Reward
In Reinforcement Learning (RL), it is commonly assumed that an immediate reward signal is generated for each action taken by the agent, helping the agent maximize cumulative rewards to obtain the optimal policy.
Generating by Understanding: Neural Visual Generation with Logical Symbol Groundings
There are two levels of symbol grounding problems among the core challenges: the first is symbol assignment, i. e. mapping latent factors of neural visual generators to semantic-meaningful symbolic factors from the reasoning systems by learning from limited labeled data.
Seeing Differently, Acting Similarly: Heterogeneously Observable Imitation Learning
In many real-world imitation learning tasks, the demonstrator and the learner have to act under different observation spaces.
Imitation Learning from Pixel-Level Demonstrations by HashReward
One of the key issues for imitation learning lies in making policy learned from limited samples to generalize well in the whole state-action space.
Preference Based Adaptation for Learning Objectives
In many real-world learning tasks, it is hard to directly optimize the true performance measures, meanwhile choosing the right surrogate objectives is also difficult.
Crowdsourcing with Unsure Option
One of the fundamental problems in crowdsourcing is the trade-off between the number of the workers needed for high-accuracy aggregation and the budget to pay.
