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Found 23 papers, 7 papers with code
Reward Design for Justifiable Sequential Decision-Making
In this work, we propose the use of a debate-based reward model for reinforcement learning agents, where the outcome of a zero-sum debate game quantifies the justifiability of a decision in a particular state.
Performative Reinforcement Learning in Gradually Shifting Environments
Unlike PRL, our framework allows to model scenarios where the environment gradually adjusts to a deployed policy.
Agent-Specific Effects: A Causal Effect Propagation Analysis in Multi-Agent MDPs
These challenges are particularly prominent in the context of multi-agent sequential decision-making, where the causal effect of an agent's action on the outcome depends on how other agents respond to that action.
Markov Decision Processes with Time-Varying Geometric Discounting
We take a game-theoretic perspective -- whereby each time step is treated as an independent decision maker with their own (fixed) discount factor -- and we study the subgame perfect equilibrium (SPE) of the resulting game as well as the related algorithmic problems.
Sequential Principal-Agent Problems with Communication: Efficient Computation and Learning
In this model, the principal and the agent interact in a stochastic environment, and each is privy to observations about the state not available to the other.
Learning Embeddings for Sequential Tasks Using Population of Agents
We present an information-theoretic framework to learn fixed-dimensional embeddings for tasks in reinforcement learning.
Implicit Poisoning Attacks in Two-Agent Reinforcement Learning: Adversarial Policies for Training-Time Attacks
In this paper, we study targeted poisoning attacks in a two-agent setting where an attacker implicitly poisons the effective environment of one of the agents by modifying the policy of its peer.
Towards Computationally Efficient Responsibility Attribution in Decentralized Partially Observable MDPs
Responsibility attribution is a key concept of accountable multi-agent decision making.
Online Reinforcement Learning with Uncertain Episode Lengths
We show that minimizing regret with this new general discounting is equivalent to minimizing regret with uncertain episode lengths.
Performative Reinforcement Learning
We introduce the framework of performative reinforcement learning where the policy chosen by the learner affects the underlying reward and transition dynamics of the environment.
Actual Causality and Responsibility Attribution in Decentralized Partially Observable Markov Decision Processes
Responsibility attribution is complementary and aims to identify the extent to which decision makers (agents) are responsible for this outcome.
Admissible Policy Teaching through Reward Design
This problem can be viewed as a dual to the problem of optimal reward poisoning attacks: instead of forcing an agent to adopt a specific policy, the reward designer incentivizes an agent to avoid taking actions that are inadmissible in certain states.
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.
On Blame Attribution for Accountable Multi-Agent Sequential Decision Making
We formalize desirable properties of blame attribution in the setting of interest, and we analyze the relationship between these properties and the studied blame attribution methods.
Reinforcement Learning for Education: Opportunities and Challenges
This survey article has grown out of the RL4ED workshop organized by the authors at the Educational Data Mining (EDM) 2021 conference.
Defense Against Reward Poisoning Attacks in Reinforcement Learning
As a threat model, we consider attacks that minimally alter rewards to make the attacker's target policy uniquely optimal under the poisoned rewards, with the optimality gap specified by an attack parameter.
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.
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.
Learning to Collaborate in Markov Decision Processes
We consider a two-agent MDP framework where agents repeatedly solve a task in a collaborative setting.
How Do Fairness Definitions Fare? Examining Public Attitudes Towards Algorithmic Definitions of Fairness
What is the best way to define algorithmic fairness?
Multi-View Decision Processes: The Helper-AI Problem
We consider a two-player sequential game in which agents have the same reward function but may disagree on the transition probabilities of an underlying Markovian model of the world.
Calibrated Fairness in Bandits
In addition, we define the {\em fairness regret}, which corresponds to the degree to which an algorithm is not calibrated, where perfect calibration requires that the probability of selecting an arm is equal to the probability with which the arm has the best quality realization.
Bayesian fairness
We consider the problem of how decision making can be fair when the underlying probabilistic model of the world is not known with certainty.
