Arrow Research search

Author name cluster

Leonidas Bakopoulos

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

3 papers
1 author row

Possible papers

3

AAMAS Conference 2026 Conference Paper

A Novel Framework for Uncertainty-Driven Adaptive Exploration

  • Leonidas Bakopoulos
  • Georgios Chalkiadakis

Adaptive exploration methods learn complex policies via alternating between exploration and exploitation. An important question for such methods is to determine the appropriate moment to switch between exploration and exploitation and vice versa. This is critical in domains that require the learning of long and complex sequences of actions. In this work, we present a generic adaptive exploration frameworkthatemploysuncertaintytoaddressthisimportantissue in a principled manner. Our framework includes previous adaptive exploration approaches as special cases. Moreover, it can incorporate any uncertainty-measuring mechanism of choice, such as mechanisms used in intrinsic motivation, or epistemic uncertaintybased exploration methods; and is experimentally shown to give rise to adaptive exploration strategies that outperform standard ones across several environments. Moreover, we showcase its potential for utilization in safety-critical domains. The code for this work can be found at https: //github. com/leoBakop/ADEU

PDF

RLC Conference 2025 Conference Paper

Seldonian Reinforcement Learning for Ad Hoc Teamwork

  • Edoardo Zorzi
  • Alberto Castellini
  • Leonidas Bakopoulos
  • Georgios Chalkiadakis
  • Alessandro Farinelli

Most offline RL algorithms return optimal policies but do not provide statistical guarantees on desirable behaviors. This could generate reliability issues in safety-critical applications, such as in some multiagent domains where agents, and possibly humans, need to interact to reach their goals without harming each other. In this work, we propose a novel offline RL approach, inspired by Seldonian optimization, which returns policies with good performance and statistically guaranteed properties with respect to predefined desirable behaviors. In particular, our focus is on Ad Hoc Teamwork settings, where agents must collaborate with new teammates without prior coordination. Our method requires only a pre-collected dataset, a set of candidate policies for our agent, and a specification about the possible policies followed by the other players---it does not require further interactions, training, or assumptions on the type and architecture of the policies. We test our algorithm in Ad Hoc Teamwork problems and show that it consistently finds reliable policies while improving sample efficiency with respect to standard ML baselines.

PDF Details

RLJ Journal 2025 Journal Article

Seldonian Reinforcement Learning for Ad Hoc Teamwork

  • Edoardo Zorzi
  • Alberto Castellini
  • Leonidas Bakopoulos
  • Georgios Chalkiadakis
  • Alessandro Farinelli

Most offline RL algorithms return optimal policies but do not provide statistical guarantees on desirable behaviors. This could generate reliability issues in safety-critical applications, such as in some multiagent domains where agents, and possibly humans, need to interact to reach their goals without harming each other. In this work, we propose a novel offline RL approach, inspired by Seldonian optimization, which returns policies with good performance and statistically guaranteed properties with respect to predefined desirable behaviors. In particular, our focus is on Ad Hoc Teamwork settings, where agents must collaborate with new teammates without prior coordination. Our method requires only a pre-collected dataset, a set of candidate policies for our agent, and a specification about the possible policies followed by the other players---it does not require further interactions, training, or assumptions on the type and architecture of the policies. We test our algorithm in Ad Hoc Teamwork problems and show that it consistently finds reliable policies while improving sample efficiency with respect to standard ML baselines.

PDF Details
v2026.09.13