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A Fully Controllable Agent in the Path Planning using Goal-Conditioned Reinforcement Learning

arXiv 2205.09967 ↗First posted 20 May 2022cs.AI cs.LGcited by 0

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Abstract (arXiv)

The aim of path planning is to reach the goal from starting point by searching for the route of an agent. In the path planning, the routes may vary depending on the number of variables such that it is important for the agent to reach various goals. Numerous studies, however, have dealt with a single goal that is predefined by the user. In the present study, I propose a novel reinforcement learning framework for a fully controllable agent in the path planning. To do this, I propose a bi-directional memory editing to obtain various bi-directional trajectories of the agent, in which the behavior of the agent and sub-goals are trained on the goal-conditioned RL. As for moving the agent in various directions, I utilize the sub-goals dedicated network, separated from a policy network. Lastly, I present the reward shaping to shorten the number of steps for the agent to reach the goal. In the experimental result, the agent was able to reach the various goals that have never been visited by the agent in the training. We confirmed that the agent could perform difficult missions such as a round trip and the agent used the shorter route with the reward shaping.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 25 of its 63 references that are in the map. First posted on arXiv 20 May 2022.

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25 of 63 references.

References · 25

  1. 2017Proximal Policy Optimization Algorithmscited by 15,215
  2. 2016Asynchronous Methods for Deep Reinforcement Learningcited by 2,258
  3. 2015Deep Reinforcement Learning with Double Q-learningcited by 1,745
  4. 2017Hindsight Experience Replaycited by 1,136
  5. 2015Prioritized Experience Replaycited by 1,099
  6. 2018Exploration by Random Network Distillationcited by 993
  7. 2018Data-Efficient Hierarchical Reinforcement Learningcited by 455
  8. 2018Visual Reinforcement Learning with Imagined Goalscited by 251
  9. 2019Search on the Replay Buffer: Bridging Planning and Reinforcement Learningcited by 194
  10. 2021Goal-Conditioned Reinforcement Learning with Imagined Subgoalscited by 134
  11. 2020C-Learning: Learning to Achieve Goals via Recursive Classificationcited by 103
  12. 2022Rethinking Goal-conditioned Supervised Learning and Its Connection to Offline RLcited by 89
13 more
  1. 2020World Model as a Graph: Learning Latent Landmarks for Planningcited by 69
  2. 2021Landmark-Guided Subgoal Generation in Hierarchical Reinforcement Learningcited by 47
  3. 2019Maximum Entropy-Regularized Multi-Goal Reinforcement Learningcited by 40
  4. 2020Mobile Robot Path Planning in Dynamic Environments through Globally Guided Reinforcement Learningcited by 37
  5. 2020Deep Reinforcement Learning based Automatic Exploration for Navigation in Unknown Environmentcited by 34
  6. 2021Self-Imitation Learning by Planningcited by 26
  7. 2021Hierarchical Reinforcement Learning with Timed Subgoalscited by 21
  8. 2020Weakly-Supervised Reinforcement Learning for Controllable Behaviorcited by 17
  9. 2020Multi-UAV Path Planning for Wireless Data Harvesting with Deep Reinforcement Learningcited by 11
  10. 2021Subgoal-based Reward Shaping to Improve Efficiency in Reinforcement Learningcited by 11
  11. 2021Hierarchies of Planning and Reinforcement Learning for Robot Navigationcited by 11
  12. 2019Hierarchical Reinforcement Learning Method for Autonomous Vehicle Behavior Planningcited by 6
  13. 2022Learning user-defined sub-goals using memory editing in reinforcement learningcited by 1

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