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.

Related papers

• PLANRL: A Motion Planning and Imitation Learning Framework to Bootstrap Reinforcement Learning [13.564676246832544]
  We introduce PLANRL, a framework that chooses when the robot should use classical motion planning and when it should learn a policy. PLANRL switches between two modes of operation: reaching a waypoint using classical techniques when away from the objects and fine-grained manipulation control when about to interact with objects. We evaluate our approach across multiple challenging simulation environments and real-world tasks, demonstrating superior performance in terms of adaptability, efficiency, and generalization compared to existing methods.
  arXiv  Detail & Related papers  (2024-08-07T19:30:08Z)
• SoNIC: Safe Social Navigation with Adaptive Conformal Inference and Constrained Reinforcement Learning [26.554847852013737]
  SoNIC is the first algorithm that integrates adaptive conformal inference and constrained reinforcement learning. Our method achieves a success rate of 96.93%, which is 11.67% higher than the previous state-of-the-art RL method. Our experiments demonstrate that the system can generate robust and socially polite decision-making when interacting with both sparse and dense crowds.
  arXiv  Detail & Related papers  (2024-07-24T17:57:21Z)
• Aquatic Navigation: A Challenging Benchmark for Deep Reinforcement Learning [53.3760591018817]
  We propose a new benchmarking environment for aquatic navigation using recent advances in the integration between game engines and Deep Reinforcement Learning. Specifically, we focus on PPO, one of the most widely accepted algorithms, and we propose advanced training techniques. Our empirical evaluation shows that a well-designed combination of these ingredients can achieve promising results.
  arXiv  Detail & Related papers  (2024-05-30T23:20:23Z)
• SERL: A Software Suite for Sample-Efficient Robotic Reinforcement Learning [82.46975428739329]
  We develop a library containing a sample efficient off-policy deep RL method, together with methods for computing rewards and resetting the environment. We find that our implementation can achieve very efficient learning, acquiring policies for PCB board assembly, cable routing, and object relocation. These policies achieve perfect or near-perfect success rates, extreme robustness even under perturbations, and exhibit emergent robustness recovery and correction behaviors.
  arXiv  Detail & Related papers  (2024-01-29T10:01:10Z)
• Efficient Reinforcement Learning via Decoupling Exploration and Utilization [6.305976803910899]
  Reinforcement Learning (RL) has achieved remarkable success across multiple fields and applications, including gaming, robotics, and autonomous vehicles. In this work, our aim is to train agent with efficient learning by decoupling exploration and utilization, so that agent can escaping the conundrum of suboptimal Solutions. The above idea is implemented in the proposed OPARL (Optimistic and Pessimistic Actor Reinforcement Learning) algorithm.
  arXiv  Detail & Related papers  (2023-12-26T09:03:23Z)
• REBEL: Reward Regularization-Based Approach for Robotic Reinforcement Learning from Human Feedback [61.54791065013767]
  A misalignment between the reward function and human preferences can lead to catastrophic outcomes in the real world. Recent methods aim to mitigate misalignment by learning reward functions from human preferences. We propose a novel concept of reward regularization within the robotic RLHF framework.
  arXiv  Detail & Related papers  (2023-12-22T04:56:37Z)
• Learning to Terminate in Object Navigation [16.164536630623644]
  This paper tackles the critical challenge of object navigation in autonomous navigation systems. We propose a novel approach, namely the Depth-Inference Termination Agent (DITA) We train our judge model along with reinforcement learning in parallel and supervise the former efficiently by reward signal.
  arXiv  Detail & Related papers  (2023-09-28T04:32:08Z)
• ReProHRL: Towards Multi-Goal Navigation in the Real World using Hierarchical Agents [1.3194749469702445]
  We present Ready for Production Hierarchical RL (ReProHRL) that divides tasks with hierarchical multi-goal navigation guided by reinforcement learning. We also use object detectors as a pre-processing step to learn multi-goal navigation and transfer it to the real world. For the real-world implementation and proof of concept demonstration, we deploy the proposed method on a nano-drone named Crazyflie with a front camera.
  arXiv  Detail & Related papers  (2023-08-17T02:23:59Z)
• 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)
• Reward Uncertainty for Exploration in Preference-based Reinforcement Learning [88.34958680436552]
  We present an exploration method specifically for preference-based reinforcement learning algorithms. Our main idea is to design an intrinsic reward by measuring the novelty based on learned reward. Our experiments show that exploration bonus from uncertainty in learned reward improves both feedback- and sample-efficiency of preference-based RL algorithms.
  arXiv  Detail & Related papers  (2022-05-24T23:22:10Z)
• Accelerating Robotic Reinforcement Learning via Parameterized Action Primitives [92.0321404272942]
  Reinforcement learning can be used to build general-purpose robotic systems. However, training RL agents to solve robotics tasks still remains challenging. In this work, we manually specify a library of robot action primitives (RAPS), parameterized with arguments that are learned by an RL policy. We find that our simple change to the action interface substantially improves both the learning efficiency and task performance.
  arXiv  Detail & Related papers  (2021-10-28T17:59:30Z)
• MADE: Exploration via Maximizing Deviation from Explored Regions [48.49228309729319]
  In online reinforcement learning (RL), efficient exploration remains challenging in high-dimensional environments with sparse rewards. We propose a new exploration approach via textitmaximizing the deviation of the occupancy of the next policy from the explored regions. Our approach significantly improves sample efficiency over state-of-the-art methods.
  arXiv  Detail & Related papers  (2021-06-18T17:57:00Z)
• Rule-Based Reinforcement Learning for Efficient Robot Navigation with Space Reduction [8.279526727422288]
  In this paper, we focus on efficient navigation with the reinforcement learning (RL) technique. We employ a reduction rule to shrink the trajectory, which in turn effectively reduces the redundant exploration space. Experiments conducted on real robot navigation problems in hex-grid environments demonstrate that RuRL can achieve improved navigation performance.
  arXiv  Detail & Related papers  (2021-04-15T07:40:27Z)
• Guided Constrained Policy Optimization for Dynamic Quadrupedal Robot Locomotion [78.46388769788405]
  We introduce guided constrained policy optimization (GCPO), an RL framework based upon our implementation of constrained policy optimization (CPPO) We show that guided constrained RL offers faster convergence close to the desired optimum resulting in an optimal, yet physically feasible, robotic control behavior without the need for precise reward function tuning.
  arXiv  Detail & Related papers  (2020-02-22T10:15:53Z)

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.