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Found 6 papers, 2 papers with code
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.
Towards Computationally Efficient Responsibility Attribution in Decentralized Partially Observable MDPs
Responsibility attribution is a key concept of accountable multi-agent decision making.
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.
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.
