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Found 11 papers, 5 papers with code
Informativeness of Reward Functions in Reinforcement Learning
Reward functions are central in specifying the task we want a reinforcement learning agent to perform.
Learning to Play Text-based Adventure Games with Maximum Entropy Reinforcement Learning
To deal with sparse extrinsic rewards from the environment, we combine it with a potential-based reward shaping technique to provide more informative (dense) reward signals to the RL agent.
Explicable Reward Design for Reinforcement Learning Agents
By being explicable, we seek to capture two properties: (a) informativeness so that the rewards speed up the agent's convergence, and (b) sparseness as a proxy for ease of interpretability of the rewards.
Curriculum Design for Teaching via Demonstrations: Theory and Applications
In particular, we study how to design a personalized curriculum over demonstrations to speed up the learner's convergence.
Policy Teaching in Reinforcement Learning via Environment Poisoning Attacks
We provide lower/upper bounds on the attack cost, and instantiate our attacks in two settings: (i) an offline setting where the agent is doing planning in the poisoned environment, and (ii) an online setting where the agent is learning a policy with poisoned feedback.
Environment Shaping in Reinforcement Learning using State Abstraction
However, the applicability of potential-based reward shaping is limited in settings where (i) the state space is very large, and it is challenging to compute an appropriate potential function, (ii) the feedback signals are noisy, and even with shaped rewards the agent could be trapped in local optima, and (iii) changing the rewards alone is not sufficient, and effective shaping requires changing the dynamics.
Policy Teaching via Environment Poisoning: Training-time Adversarial Attacks against Reinforcement Learning
We study a security threat to reinforcement learning where an attacker poisons the learning environment to force the agent into executing a target policy chosen by the attacker.
Understanding the Power and Limitations of Teaching with Imperfect Knowledge
Machine teaching studies the interaction between a teacher and a student/learner where the teacher selects training examples for the learner to learn a specific task.
Learner-aware Teaching: Inverse Reinforcement Learning with Preferences and Constraints
We study two teaching approaches: learner-agnostic teaching, where the teacher provides demonstrations from an optimal policy ignoring the learner's preferences, and learner-aware teaching, where the teacher accounts for the learner's preferences.
Interactive Teaching Algorithms for Inverse Reinforcement Learning
We study the problem of inverse reinforcement learning (IRL) with the added twist that the learner is assisted by a helpful teacher.
Learning to Collaborate in Markov Decision Processes
We consider a two-agent MDP framework where agents repeatedly solve a task in a collaborative setting.
