MOReL : Model-Based Offline Reinforcement Learning

NIPS 2020, 2020.

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Eps-3 Gauss-1 Gauss-3continuous controloffline reinforcement learningmaximum mean discrepancyreinforcement learning更多(13+)
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Our results suggest that MOReL with the pessimistic Markov Decision Processes construction significantly outperforms naive model-based RL

摘要

In offline reinforcement learning (RL), the goal is to learn a successful policy using only a dataset of historical interactions with the environment, without any additional online interactions. This serves as an extreme test for an agent's ability to effectively use historical data, which is critical for efficient RL. Prior work in off...更多

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简介
  • The availability and use of large datasets have enabled tremendous advances in computer vision [1], speech recognition [2], and natural language processing [3, 4].
  • In these fields, it is customary to first collect large datasets [5, 6, 7], train deep learning models on these datasets, and deploy these models on various platforms.
  • Similar to progress in other fields of AI, the ability to effectively learn from large offline datasets may hold the key to unlocking the sample efficiency of RL agents
重点内容
  • The availability and use of large datasets have enabled tremendous advances in computer vision [1], speech recognition [2], and natural language processing [3, 4]
  • Similar to progress in other fields of AI, the ability to effectively learn from large offline datasets may hold the key to unlocking the sample efficiency of reinforcement learning (RL) agents
  • We evaluate our algorithm in the standard continuous control benchmarks in OpenAI gym modified for the batch setting as done in a number of recent works [16, 17, 20], and find that our algorithm obtains state of the art (SOTA) results in a majority of the tasks
  • Our results suggest that MOReL with the pessimistic Markov Decision Processes (MDP) construction significantly outperforms naive model-based RL (MBRL)
  • We introduced a new model based framework MOReL for the offline RL problem
  • MOReL incorporates both generalization and pessimism helping it perform policy search in known states that may not directly occur in the static offline dataset but can be predicted using the dataset, and at the same time do not drift into unknown states that cannot be predicted using the static offline data
方法
  • Environments and partially trained policies: Following recent works in offline RL [16, 17, 20], the authors consider four continuous control tasks: Hopper-v2, HalfCheetah-v2, Ant-v2, Walker2d-v2 from OpenAI gym [77] simulated with MuJoCo [78].
  • The authors typically have access to data collected using a partially trained sub-optimal policy interacting with the environment
  • To simulate this setting, following guidelines from prior work [16, 17, 20], the authors obtain a partially trained policy πp by running TRPO [65] in these environments until the policy reaches a value of 1000, 4000, 1000, 1000 respectively for the four environments.
结果
  • The authors' results suggest that MOReL with the pessimistic MDP construction significantly outperforms naive MBRL.
结论
  • The authors introduced a new model based framework MOReL for the offline RL problem.
  • The modular structure of MOReL comprising of model learning, uncertainty estimation and plannning allows the use of a variety of approaches in each of these modules.
  • While the instantiation of MOReL in this paper uses simple and standard approaches, an interesting direction for future work is to explore the benefits of more sophisticated approaches such as multi-step prediction for model learning, prediction with abstention for uncertainty estimation and so on.
  • MOReL’s modular structure allows it to automatically benefit from future progress in any of the modules
总结
  • Introduction:

    The availability and use of large datasets have enabled tremendous advances in computer vision [1], speech recognition [2], and natural language processing [3, 4].
  • In these fields, it is customary to first collect large datasets [5, 6, 7], train deep learning models on these datasets, and deploy these models on various platforms.
  • Similar to progress in other fields of AI, the ability to effectively learn from large offline datasets may hold the key to unlocking the sample efficiency of RL agents
  • Objectives:

    The authors aim to design algorithms that would result in as low a sub-optimality as possible.
  • Methods:

    Environments and partially trained policies: Following recent works in offline RL [16, 17, 20], the authors consider four continuous control tasks: Hopper-v2, HalfCheetah-v2, Ant-v2, Walker2d-v2 from OpenAI gym [77] simulated with MuJoCo [78].
  • The authors typically have access to data collected using a partially trained sub-optimal policy interacting with the environment
  • To simulate this setting, following guidelines from prior work [16, 17, 20], the authors obtain a partially trained policy πp by running TRPO [65] in these environments until the policy reaches a value of 1000, 4000, 1000, 1000 respectively for the four environments.
  • Results:

    The authors' results suggest that MOReL with the pessimistic MDP construction significantly outperforms naive MBRL.
  • Conclusion:

    The authors introduced a new model based framework MOReL for the offline RL problem.
  • The modular structure of MOReL comprising of model learning, uncertainty estimation and plannning allows the use of a variety of approaches in each of these modules.
  • While the instantiation of MOReL in this paper uses simple and standard approaches, an interesting direction for future work is to explore the benefits of more sophisticated approaches such as multi-step prediction for model learning, prediction with abstention for uncertainty estimation and so on.
  • MOReL’s modular structure allows it to automatically benefit from future progress in any of the modules
表格
  • Table1: Results in the four environments and five exploration configurations. 0 represents overflow/divergence for Q-learning based algorithms
  • Table2: Value of the policy outputted by MOReL when working with a dataset collected with a random policy (Pure-random) and a partially trained policy (Pure-partial). The value of the behavior policy is indicated within the parenthesis. All results are averaged over 5 random seeds
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相关工作
  • Our work takes a model-based approach to offline RL. We review related work pertaining to both of these domains in this section.

    2.1 The offline RL setting

    Offline RL, as a problem setting, dates at least to the work of Lange et al [11]. In this setting, an RL agent is provided access to a typically large offline dataset, using which it has to produce a highly rewarding policy. This has direct applications in fields like healthcare [34, 35, 36], recommendation systems [37, 38, 39, 40], dialogue systems [41, 19, 42], and autonomous driving [43]. We refer the readers to the review paper of Levine et al [44] for an overview of potential applications. On the algorithmic front, prior work in offline RL can be broadly categorized into three groups as described below.
基金
  • Rahul Kidambi acknowledges funding from NSF Award CCF − 1740822
  • Thorsten Joachims acknowledges funding from NSF Award IIS − 1901168
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