82 citations · 98 across the 26 of their papers we have counts for
6 papers · 1 filter
On Feasible Rewards in Multi-Agent Inverse Reinforcement Learning
Till Freihaut, Giorgia Ramponi
Multi-agent Inverse Reinforcement Learning (MAIRL) aims to recover agent reward functions from expert demonstrations. We characterize the feasible reward set in Markov games, ident…
Learning Collusion in Episodic, Inventory-Constrained Markets
Paul Friedrich, Barna Pásztor, Giorgia Ramponi
Pricing algorithms have demonstrated the capability to learn tacit collusion that is largely unaddressed by current regulations. Their increasing use in markets, including oligopol…
On the Convergence of Single-Timescale Actor-Critic
Navdeep Kumar, Priyank Agrawal, Giorgia Ramponi +2
We analyze the global convergence of the single-timescale actor-critic (AC) algorithm for the infinite-horizon discounted Markov Decision Processes (MDPs) with finite state spaces.…
Preference Elicitation for Offline Reinforcement Learning
Alizée Pace, Bernhard Schölkopf, Gunnar Rätsch +1
Applying reinforcement learning (RL) to real-world problems is often made challenging by the inability to interact with the environment and the difficulty of designing reward funct…
Contextual Bilevel Reinforcement Learning for Incentive Alignment
Vinzenz Thoma, Barna Pasztor, Andreas Krause +2
The optimal policy in various real-world strategic decision-making problems depends both on the environmental configuration and exogenous events. For these settings, we introduce C…
Truly No-Regret Learning in Constrained MDPs
Adrian Müller, Pragnya Alatur, Volkan Cevher +2
Constrained Markov decision processes (CMDPs) are a common way to model safety constraints in reinforcement learning. State-of-the-art methods for efficiently solving CMDPs are bas…