CCE: Sample Efficient Sparse Reward Policy Learning for Robotic Navigation via Confidence-Controlled Exploration

• URL: http://arxiv.org/abs/2306.06192v8
• Date: Tue, 24 Sep 2024 01:09:54 GMT
• Title: CCE: Sample Efficient Sparse Reward Policy Learning for Robotic Navigation via Confidence-Controlled Exploration
• Authors: Bhrij Patel, Kasun Weerakoon, Wesley A. Suttle, Alec Koppel, Brian M. Sadler, Tianyi Zhou, Amrit Singh Bedi, Dinesh Manocha,
• Abstract summary: Confidence-Controlled Exploration (CCE) is designed to enhance the training sample efficiency of reinforcement learning algorithms for sparse reward settings such as robot navigation. CCE is based on a novel relationship we provide between gradient estimation and policy entropy. We demonstrate through simulated and real-world experiments that CCE outperforms conventional methods that employ constant trajectory lengths and entropy regularization.
• Score: 72.24964965882783
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We introduce Confidence-Controlled Exploration (CCE), a novel exploration scheme designed to enhance the training sample efficiency of reinforcement learning (RL) algorithms for sparse reward settings such as robot navigation. Sparse rewards are common in RL and convenient to design and implement, but typically hard to deal with due to the challenges of exploration. Existing methods deploy regularization-based methods to deal with the exploration challenges. However, it is hard to characterize the balance between exploration and exploitation because regularization modifies the reward function itself, hence changing the objective we are optimizing for. In contrast to regularization-based approaches in the existing literature, our approach, CCE, is based on a novel relationship we provide between gradient estimation and policy entropy. CCE dynamically adjusts the number of samples of the gradient update used during training to control exploration. Interestingly, CCE can be applied to both existing on-policy and off-policy RL methods, which we demonstrate by empirically validating its efficacy on three popular RL methods: REINFORCE, Proximal Policy Optimization (PPO), and Soft Actor-Critic (SAC) for goal-reaching robotic navigation tasks. We demonstrate through simulated and real-world experiments that CCE outperforms conventional methods that employ constant trajectory lengths and entropy regularization when constraining the sample budget. For a fixed sample budget, CCE achieves an 18\% increase in navigation success rate, a 20-38\% reduction in navigation path length, and a 9.32\% decrease in elevation costs. Furthermore, we showcase the versatility of CCE by integrating it with the Clearpath Husky robot, illustrating its applicability in complex outdoor environments.

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