Diversity for Contingency: Learning Diverse Behaviors for Efficient Adaptation and Transfer

• URL: http://arxiv.org/abs/2310.07493v1
• Date: Wed, 11 Oct 2023 13:39:35 GMT
• Title: Diversity for Contingency: Learning Diverse Behaviors for Efficient Adaptation and Transfer
• Authors: Finn Rietz and Johannes Andreas Stork
• Abstract summary: We propose a simple method for discovering all possible solutions of a given task. Unlike prior methods, our approach does not require learning additional models for novelty detection.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Discovering all useful solutions for a given task is crucial for transferable RL agents, to account for changes in the task or transition dynamics. This is not considered by classical RL algorithms that are only concerned with finding the optimal policy, given the current task and dynamics. We propose a simple method for discovering all possible solutions of a given task, to obtain an agent that performs well in the transfer setting and adapts quickly to changes in the task or transition dynamics. Our method iteratively learns a set of policies, while each subsequent policy is constrained to yield a solution that is unlikely under all previous policies. Unlike prior methods, our approach does not require learning additional models for novelty detection and avoids balancing task and novelty reward signals, by directly incorporating the constraint into the action selection and optimization steps.

Related papers

• HarmoDT: Harmony Multi-Task Decision Transformer for Offline Reinforcement Learning [72.25707314772254]
  We introduce the Harmony Multi-Task Decision Transformer (HarmoDT), a novel solution designed to identify an optimal harmony subspace of parameters for each task. The upper level of this framework is dedicated to learning a task-specific mask that delineates the harmony subspace, while the inner level focuses on updating parameters to enhance the overall performance of the unified policy.
  arXiv  Detail & Related papers  (2024-05-28T11:41:41Z)
• 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)
• Stepsize Learning for Policy Gradient Methods in Contextual Markov Decision Processes [35.889129338603446]
  Policy-based algorithms are among the most widely adopted techniques in model-free RL. They tend to struggle when asked to accomplish a series of heterogeneous tasks. We introduce a new formulation, known as meta-MDP, that can be used to solve any hyper parameter selection problem in RL.
  arXiv  Detail & Related papers  (2023-06-13T12:58:12Z)
• Sample-Efficient Multi-Objective Learning via Generalized Policy Improvement Prioritization [8.836422771217084]
  Multi-objective reinforcement learning (MORL) algorithms tackle sequential decision problems where agents may have different preferences. We introduce a novel algorithm that uses Generalized Policy Improvement (GPI) to define principled, formally-derived prioritization schemes. We empirically show that our method outperforms state-of-the-art MORL algorithms in challenging multi-objective tasks.
  arXiv  Detail & Related papers  (2023-01-18T20:54:40Z)
• Efficient Meta Reinforcement Learning for Preference-based Fast Adaptation [17.165083095799712]
  We study the problem of few-shot adaptation in the context of human-in-the-loop reinforcement learning. We develop a meta-RL algorithm that enables fast policy adaptation with preference-based feedback.
  arXiv  Detail & Related papers  (2022-11-20T03:55:09Z)
• 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)
• Adaptive Policy Transfer in Reinforcement Learning [9.594432031144715]
  We introduce a principled mechanism that can "Adapt-to-Learn", that is adapt the source policy to learn to solve a target task. We show that the presented method learns to seamlessly combine learning from adaptation and exploration and leads to a robust policy transfer algorithm.
  arXiv  Detail & Related papers  (2021-05-10T22:42:03Z)
• MetaVIM: Meta Variationally Intrinsic Motivated Reinforcement Learning for Decentralized Traffic Signal Control [54.162449208797334]
  Traffic signal control aims to coordinate traffic signals across intersections to improve the traffic efficiency of a district or a city. Deep reinforcement learning (RL) has been applied to traffic signal control recently and demonstrated promising performance where each traffic signal is regarded as an agent. We propose a novel Meta Variationally Intrinsic Motivated (MetaVIM) RL method to learn the decentralized policy for each intersection that considers neighbor information in a latent way.
  arXiv  Detail & Related papers  (2021-01-04T03:06:08Z)
• One Solution is Not All You Need: Few-Shot Extrapolation via Structured MaxEnt RL [142.36621929739707]
  We show that learning diverse behaviors for accomplishing a task can lead to behavior that generalizes to varying environments. By identifying multiple solutions for the task in a single environment during training, our approach can generalize to new situations.
  arXiv  Detail & Related papers  (2020-10-27T17:41:57Z)
• Learning Adaptive Exploration Strategies in Dynamic Environments Through Informed Policy Regularization [100.72335252255989]
  We study the problem of learning exploration-exploitation strategies that effectively adapt to dynamic environments. We propose a novel algorithm that regularizes the training of an RNN-based policy using informed policies trained to maximize the reward in each task.
  arXiv  Detail & Related papers  (2020-05-06T16:14:48Z)
• Efficient Deep Reinforcement Learning via Adaptive Policy Transfer [50.51637231309424]
  Policy Transfer Framework (PTF) is proposed to accelerate Reinforcement Learning (RL) Our framework learns when and which source policy is the best to reuse for the target policy and when to terminate it. Experimental results show it significantly accelerates the learning process and surpasses state-of-the-art policy transfer methods.
  arXiv  Detail & Related papers  (2020-02-19T07:30:57Z)

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.