Dealing with Sparse Rewards Using Graph Neural Networks

• URL: http://arxiv.org/abs/2203.13424v2
• Date: Sun, 15 Oct 2023 23:26:05 GMT
• Title: Dealing with Sparse Rewards Using Graph Neural Networks
• Authors: Matvey Gerasyov, Ilya Makarov
• Abstract summary: We propose two modifications of one of the recent reward shaping methods based on graph convolutional networks. We empirically validate the effectiveness of our solutions for the task of navigation in a 3D environment with sparse rewards. For the solution featuring attention mechanism, we are also able to show that the learned attention is concentrated on edges corresponding to important transitions in 3D environment.
• Score: 0.15540058359482856
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Deep reinforcement learning in partially observable environments is a difficult task in itself, and can be further complicated by a sparse reward signal. Most tasks involving navigation in three-dimensional environments provide the agent with extremely limited information. Typically, the agent receives a visual observation input from the environment and is rewarded once at the end of the episode. A good reward function could substantially improve the convergence of reinforcement learning algorithms for such tasks. The classic approach to increase the density of the reward signal is to augment it with supplementary rewards. This technique is called the reward shaping. In this study, we propose two modifications of one of the recent reward shaping methods based on graph convolutional networks: the first involving advanced aggregation functions, and the second utilizing the attention mechanism. We empirically validate the effectiveness of our solutions for the task of navigation in a 3D environment with sparse rewards. For the solution featuring attention mechanism, we are also able to show that the learned attention is concentrated on edges corresponding to important transitions in 3D environment.

Related papers

• Active search and coverage using point-cloud reinforcement learning [50.741409008225766]
  This paper presents an end-to-end deep reinforcement learning solution for target search and coverage. We show that deep hierarchical feature learning works for RL and that by using farthest point sampling (FPS) we can reduce the amount of points. We also show that multi-head attention for point-clouds helps to learn the agent faster but converges to the same outcome.
  arXiv  Detail & Related papers  (2023-12-18T18:16:30Z)
• Self-Supervised Monocular Depth Estimation by Direction-aware Cumulative Convolution Network [80.19054069988559]
  We find that self-supervised monocular depth estimation shows a direction sensitivity and environmental dependency. We propose a new Direction-aware Cumulative Convolution Network (DaCCN), which improves the depth representation in two aspects. Experiments show that our method achieves significant improvements on three widely used benchmarks.
  arXiv  Detail & Related papers  (2023-08-10T14:32:18Z)
• Unleash the Potential of Image Branch for Cross-modal 3D Object Detection [67.94357336206136]
  We present a new cross-modal 3D object detector, namely UPIDet, which aims to unleash the potential of the image branch from two aspects. First, UPIDet introduces a new 2D auxiliary task called normalized local coordinate map estimation. Second, we discover that the representational capability of the point cloud backbone can be enhanced through the gradients backpropagated from the training objectives of the image branch.
  arXiv  Detail & Related papers  (2023-01-22T08:26:58Z)
• Learning Dense Reward with Temporal Variant Self-Supervision [5.131840233837565]
  Complex real-world robotic applications lack explicit and informative descriptions that can directly be used as rewards. Previous effort has shown that it is possible to algorithmically extract dense rewards directly from multimodal observations. This paper proposes a more efficient and robust way of sampling and learning.
  arXiv  Detail & Related papers  (2022-05-20T20:30:57Z)
• Point Discriminative Learning for Unsupervised Representation Learning on 3D Point Clouds [54.31515001741987]
  We propose a point discriminative learning method for unsupervised representation learning on 3D point clouds. We achieve this by imposing a novel point discrimination loss on the middle level and global level point features. Our method learns powerful representations and achieves new state-of-the-art performance.
  arXiv  Detail & Related papers  (2021-08-04T15:11:48Z)
• Reward prediction for representation learning and reward shaping [0.8883733362171032]
  We propose learning a state representation in a self-supervised manner for reward prediction. We augment the training of out-of-the-box RL agents by shaping the reward using our reward predictor during policy learning.
  arXiv  Detail & Related papers  (2021-05-07T11:29:32Z)
• MaAST: Map Attention with Semantic Transformersfor Efficient Visual Navigation [4.127128889779478]
  This work focuses on performing better or comparable to the existing learning-based solutions for visual navigation for autonomous agents. We propose a method to encode vital scene semantics into a semantically informed, top-down egocentric map representation. We conduct experiments on 3-D reconstructed indoor PointGoal visual navigation and demonstrate the effectiveness of our approach.
  arXiv  Detail & Related papers  (2021-03-21T12:01:23Z)
• Variational Structured Attention Networks for Deep Visual Representation Learning [49.80498066480928]
  We propose a unified deep framework to jointly learn both spatial attention maps and channel attention in a principled manner. Specifically, we integrate the estimation and the interaction of the attentions within a probabilistic representation learning framework. We implement the inference rules within the neural network, thus allowing for end-to-end learning of the probabilistic and the CNN front-end parameters.
  arXiv  Detail & Related papers  (2021-03-05T07:37:24Z)
• Learning Intrinsic Symbolic Rewards in Reinforcement Learning [7.101885582663675]
  We present a method that discovers dense rewards in the form of low-dimensional symbolic trees. We show that the discovered dense rewards are an effective signal for an RL policy to solve the benchmark tasks.
  arXiv  Detail & Related papers  (2020-10-08T00:02:46Z)
• Reward Propagation Using Graph Convolutional Networks [61.32891095232801]
  We propose a new framework for learning potential functions by leveraging ideas from graph representation learning. Our approach relies on Graph Convolutional Networks which we use as a key ingredient in combination with the probabilistic inference view of reinforcement learning.
  arXiv  Detail & Related papers  (2020-10-06T04:38:16Z)

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.