Formal Ethical Obligations in Reinforcement Learning Agents: Verification and Policy Updates

• URL: http://arxiv.org/abs/2408.00147v1
• Date: Wed, 31 Jul 2024 20:21:15 GMT
• Title: Formal Ethical Obligations in Reinforcement Learning Agents: Verification and Policy Updates
• Authors: Colin Shea-Blymyer, Houssam Abbas,
• Abstract summary: Designers need tools to automatically reason about what agents ought to do, how that conflicts with what is actually happening, and how a policy might be modified to remove the conflict. We propose a new deontic logic, Expected Act Utilitarian deontic logic, for enabling this reasoning at design time. Unlike approaches that work at the reward level, working at the logical level increases the transparency of the trade-offs.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: When designing agents for operation in uncertain environments, designers need tools to automatically reason about what agents ought to do, how that conflicts with what is actually happening, and how a policy might be modified to remove the conflict. These obligations include ethical and social obligations, permissions and prohibitions, which constrain how the agent achieves its mission and executes its policy. We propose a new deontic logic, Expected Act Utilitarian deontic logic, for enabling this reasoning at design time: for specifying and verifying the agent's strategic obligations, then modifying its policy from a reference policy to meet those obligations. Unlike approaches that work at the reward level, working at the logical level increases the transparency of the trade-offs. We introduce two algorithms: one for model-checking whether an RL agent has the right strategic obligations, and one for modifying a reference decision policy to make it meet obligations expressed in our logic. We illustrate our algorithms on DAC-MDPs which accurately abstract neural decision policies, and on toy gridworld environments.

Related papers

• Few-shot Policy (de)composition in Conversational Question Answering [54.259440408606515]
  We propose a neuro-symbolic framework to detect policy compliance using large language models (LLMs) in a few-shot setting. We show that our approach soundly reasons about policy compliance conversations by extracting sub-questions to be answered, assigning truth values from contextual information, and explicitly producing a set of logic statements from the given policies. We apply this approach to the popular PCD and conversational machine reading benchmark, ShARC, and show competitive performance with no task-specific finetuning.
  arXiv  Detail & Related papers  (2025-01-20T08:40:15Z)
• Adaptive Opponent Policy Detection in Multi-Agent MDPs: Real-Time Strategy Switch Identification Using Running Error Estimation [1.079960007119637]
  OPS-DeMo is an online algorithm that employs dynamic error decay to detect changes in opponents' policies. Our approach outperforms PPO-trained models in dynamic scenarios like the Predator-Prey setting.
  arXiv  Detail & Related papers  (2024-06-10T17:34:44Z)
• Dual policy as self-model for planning [71.73710074424511]
  We refer to the model used to simulate one's decisions as the agent's self-model. Inspired by current reinforcement learning approaches and neuroscience, we explore the benefits and limitations of using a distilled policy network as the self-model.
  arXiv  Detail & Related papers  (2023-06-07T13:58:45Z)
• Boolean Decision Rules for Reinforcement Learning Policy Summarisation [16.969788244589388]
  We create a rule-based summary of an agent's policy using a lava gridworld. We discuss possible avenues to introduce safety into a RL agent's policy by using rules generated by this rule-based model as constraints imposed on the agent's policy.
  arXiv  Detail & Related papers  (2022-07-18T14:51:24Z)
• Formalizing the Problem of Side Effect Regularization [81.97441214404247]
  We propose a formal criterion for side effect regularization via the assistance game framework. In these games, the agent solves a partially observable Markov decision process. We show that this POMDP is solved by trading off the proxy reward with the agent's ability to achieve a range of future tasks.
  arXiv  Detail & Related papers  (2022-06-23T16:36:13Z)
• Distributed Adaptive Learning Under Communication Constraints [54.22472738551687]
  This work examines adaptive distributed learning strategies designed to operate under communication constraints. We consider a network of agents that must solve an online optimization problem from continual observation of streaming data.
  arXiv  Detail & Related papers  (2021-12-03T19:23:48Z)
• SPOTTER: Extending Symbolic Planning Operators through Targeted Reinforcement Learning [24.663586662594703]
  Symbolic planning models allow decision-making agents to sequence actions in arbitrary ways to achieve a variety of goals in dynamic domains. Reinforcement learning approaches do not require such models, and instead learn domain dynamics by exploring the environment and collecting rewards. We propose an integrated framework named SPOTTER that uses RL to augment and support ("spot") a planning agent by discovering new operators needed to accomplish goals that are initially unreachable for the agent.
  arXiv  Detail & Related papers  (2020-12-24T00:31:02Z)
• Verifiable Planning in Expected Reward Multichain MDPs [20.456052208569115]
  We explore the steady-state planning problem of deriving a decision-making policy for an agent. We prove that optimal solutions to the proposed programs yield stationary policies with rigorous guarantees of behavior.
  arXiv  Detail & Related papers  (2020-12-03T18:54:24Z)
• Implicit Distributional Reinforcement Learning [61.166030238490634]
  implicit distributional actor-critic (IDAC) built on two deep generator networks (DGNs) Semi-implicit actor (SIA) powered by a flexible policy distribution. We observe IDAC outperforms state-of-the-art algorithms on representative OpenAI Gym environments.
  arXiv  Detail & Related papers  (2020-07-13T02:52:18Z)
• Learning Goal-oriented Dialogue Policy with Opposite Agent Awareness [116.804536884437]
  We propose an opposite behavior aware framework for policy learning in goal-oriented dialogues. We estimate the opposite agent's policy from its behavior and use this estimation to improve the target agent by regarding it as part of the target policy.
  arXiv  Detail & Related papers  (2020-04-21T03:13:44Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.