Imaginary Hindsight Experience Replay: Curious Model-based Learning for Sparse Reward Tasks

• URL: http://arxiv.org/abs/2110.02414v2
• Date: Wed, 9 Aug 2023 09:29:26 GMT
• Title: Imaginary Hindsight Experience Replay: Curious Model-based Learning for Sparse Reward Tasks
• Authors: Robert McCarthy, Qiang Wang, Stephen J. Redmond
• Abstract summary: We propose a model-based method tailored for sparse-reward tasks that foregoes the need for complicated reward engineering. This approach, termed Imaginary Hindsight Experience Replay, minimises real-world interactions by incorporating imaginary data into policy updates. Upon evaluation, this approach provides an order of magnitude increase in data-efficiency on average versus the state-of-the-art model-free method in the benchmark OpenAI Gym Fetch Robotics tasks.
• Score: 9.078290260836706
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Model-based reinforcement learning is a promising learning strategy for practical robotic applications due to its improved data-efficiency versus model-free counterparts. However, current state-of-the-art model-based methods rely on shaped reward signals, which can be difficult to design and implement. To remedy this, we propose a simple model-based method tailored for sparse-reward multi-goal tasks that foregoes the need for complicated reward engineering. This approach, termed Imaginary Hindsight Experience Replay, minimises real-world interactions by incorporating imaginary data into policy updates. To improve exploration in the sparse-reward setting, the policy is trained with standard Hindsight Experience Replay and endowed with curiosity-based intrinsic rewards. Upon evaluation, this approach provides an order of magnitude increase in data-efficiency on average versus the state-of-the-art model-free method in the benchmark OpenAI Gym Fetch Robotics tasks.

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