"What are my options?": Explaining RL Agents with Diverse Near-Optimal Alternatives (Extended)

• URL: http://arxiv.org/abs/2506.09901v1
• Date: Wed, 11 Jun 2025 16:15:56 GMT
• Title: "What are my options?": Explaining RL Agents with Diverse Near-Optimal Alternatives (Extended)
• Authors: Noel Brindise, Vijeth Hebbar, Riya Shah, Cedric Langbort,
• Abstract summary: We provide an extended discussion of a new approach to explainable Reinforcement Learning called Diverse Near-optimal Alternatives (DNA)<n>DNA seeks a set of reasonable "options" for trajectory-planning agents, optimizing policies to produce qualitatively diverse trajectories in Euclidean space.<n>We show that DNA successfully returns qualitatively different policies that constitute meaningfully different "options" in simulation.
• Score: 0.19999259391104385
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In this work, we provide an extended discussion of a new approach to explainable Reinforcement Learning called Diverse Near-Optimal Alternatives (DNA), first proposed at L4DC 2025. DNA seeks a set of reasonable "options" for trajectory-planning agents, optimizing policies to produce qualitatively diverse trajectories in Euclidean space. In the spirit of explainability, these distinct policies are used to "explain" an agent's options in terms of available trajectory shapes from which a human user may choose. In particular, DNA applies to value function-based policies on Markov decision processes where agents are limited to continuous trajectories. Here, we describe DNA, which uses reward shaping in local, modified Q-learning problems to solve for distinct policies with guaranteed epsilon-optimality. We show that it successfully returns qualitatively different policies that constitute meaningfully different "options" in simulation, including a brief comparison to related approaches in the stochastic optimization field of Quality Diversity. Beyond the explanatory motivation, this work opens new possibilities for exploration and adaptive planning in RL.

Related papers

• IOB: Integrating Optimization Transfer and Behavior Transfer for Multi-Policy Reuse [50.90781542323258]
  Reinforcement learning (RL) agents can transfer knowledge from source policies to a related target task. Previous methods introduce additional components, such as hierarchical policies or estimations of source policies' value functions. We propose a novel transfer RL method that selects the source policy without training extra components.
  arXiv  Detail & Related papers  (2023-08-14T09:22:35Z)
• Reparameterized Policy Learning for Multimodal Trajectory Optimization [61.13228961771765]
  We investigate the challenge of parametrizing policies for reinforcement learning in high-dimensional continuous action spaces. We propose a principled framework that models the continuous RL policy as a generative model of optimal trajectories. We present a practical model-based RL method, which leverages the multimodal policy parameterization and learned world model.
  arXiv  Detail & Related papers  (2023-07-20T09:05:46Z)
• Acceleration in Policy Optimization [50.323182853069184]
  We work towards a unifying paradigm for accelerating policy optimization methods in reinforcement learning (RL) by integrating foresight in the policy improvement step via optimistic and adaptive updates. We define optimism as predictive modelling of the future behavior of a policy, and adaptivity as taking immediate and anticipatory corrective actions to mitigate errors from overshooting predictions or delayed responses to change. We design an optimistic policy gradient algorithm, adaptive via meta-gradient learning, and empirically highlight several design choices pertaining to acceleration, in an illustrative task.
  arXiv  Detail & Related papers  (2023-06-18T15:50:57Z)
• Diverse Policy Optimization for Structured Action Space [59.361076277997704]
  We propose Diverse Policy Optimization (DPO) to model the policies in structured action space as the energy-based models (EBM) A novel and powerful generative model, GFlowNet, is introduced as the efficient, diverse EBM-based policy sampler. Experiments on ATSC and Battle benchmarks demonstrate that DPO can efficiently discover surprisingly diverse policies.
  arXiv  Detail & Related papers  (2023-02-23T10:48:09Z)
• Optimistic Linear Support and Successor Features as a Basis for Optimal Policy Transfer [7.970144204429356]
  We introduce an SF-based extension of the Optimistic Linear Support algorithm to learn a set of policies whose SFs form a convex coverage set. We prove that policies in this set can be combined via generalized policy improvement to construct optimal behaviors for any new linearly-expressible tasks.
  arXiv  Detail & Related papers  (2022-06-22T19:00:08Z)
• A Parametric Class of Approximate Gradient Updates for Policy Optimization [47.69337420768319]
  We develop a unified perspective that re-expresses the underlying updates in terms of a limited choice of gradient form and scaling function. We obtain novel yet well motivated updates that generalize existing algorithms in a way that can deliver benefits both in terms of convergence speed and final result quality.
  arXiv  Detail & Related papers  (2022-06-17T01:28:38Z)
• Continuously Discovering Novel Strategies via Reward-Switching Policy Optimization [9.456388509414046]
  Reward-Switching Policy Optimization (RSPO) RSPO is a paradigm to discover diverse strategies in complex RL environments by iteratively finding novel policies that are both locally optimal and sufficiently different from existing ones. Experiments show that RSPO is able to discover a wide spectrum of strategies in a variety of domains, ranging from single-agent particle-world tasks and MuJoCo continuous control to multi-agent stag-hunt games and StarCraftII challenges.
  arXiv  Detail & Related papers  (2022-04-04T12:38:58Z)
• Optimistic Policy Optimization is Provably Efficient in Non-stationary MDPs [113.8752163061151]
  We study episodic reinforcement learning (RL) in non-stationary linear kernel Markov decision processes (MDPs)<n>We propose the underlineperiodically underlinerestarted underlineoptimistic underlinepolicy underlineoptimization algorithm (PROPO)<n>PROPO features two mechanisms: sliding-window-based policy evaluation and periodic-restart-based policy improvement.
  arXiv  Detail & Related papers  (2021-10-18T02:33:20Z)
• Discovering Diverse Nearly Optimal Policies withSuccessor Features [30.144946007098852]
  In reinforcement learning, a set of diverse policies can be useful for exploration, transfer, hierarchy, and robustness. We propose Diverse Successive Policies, a method for discovering policies that are diverse in the space of Successor Features.
  arXiv  Detail & Related papers  (2021-06-01T17:56:13Z)
• Policy Manifold Search: Exploring the Manifold Hypothesis for Diversity-based Neuroevolution [4.920145245773581]
  This paper proposes a novel method for diversity-based policy search via Neuroevolution. We use the Quality-Diversity framework which provides a principled approach to policy search. We also use the Jacobian of the inverse-mapping function to guide the search in the representation space.
  arXiv  Detail & Related papers  (2021-04-27T18:52:03Z)
• SOAC: The Soft Option Actor-Critic Architecture [25.198302636265286]
  Methods have been proposed for concurrently learning low-level intra-option policies and high-level option selection policy. Existing methods typically suffer from two major challenges: ineffective exploration and unstable updates. We present a novel and stable off-policy approach that builds on the maximum entropy model to address these challenges.
  arXiv  Detail & Related papers  (2020-06-25T13:06:59Z)

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.