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Found 6 papers, 2 papers with code
Select to Perfect: Imitating desired behavior from large multi-agent data
Desired characteristics for an AI agent can be expressed by assigning desirability scores, which we assume are not assigned to individual behaviors but to collective trajectories.
Rethinking Out-of-Distribution Detection for Reinforcement Learning: Advancing Methods for Evaluation and Detection
In this paper, we study the problem of out-of-distribution (OOD) detection in RL, which focuses on identifying situations at test time that RL agents have not encountered in their training environments.
Out-of-Distribution Detection
Out of Distribution (OOD) Detection
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Extracting Reward Functions from Diffusion Models
Diffusion models have achieved remarkable results in image generation, and have similarly been used to learn high-performing policies in sequential decision-making tasks.
Learn what matters: cross-domain imitation learning with task-relevant embeddings
We study how an autonomous agent learns to perform a task from demonstrations in a different domain, such as a different environment or different agent.
Illusory Attacks: Information-Theoretic Detectability Matters in Adversarial Attacks
Autonomous agents deployed in the real world need to be robust against adversarial attacks on sensory inputs.
Learning Altruistic Behaviours in Reinforcement Learning without External Rewards
Such an approach assumes that other agents' goals are known so that the altruistic agent can cooperate in achieving those goals.
