Core Concepts
The proposed Biologically-Plausible Topology improved Spiking Actor Network (BPT-SAN) integrates spiking neurons with intricate spatial-temporal dynamics and network topologies featuring biologically-plausible connectivity patterns, enhancing the network's information processing capability for efficient decision-making in deep reinforcement learning.
Abstract
The content introduces the Biologically-Plausible Topology improved Spiking Actor Network (BPT-SAN) for efficient decision-making in deep reinforcement learning (DRL).
The key highlights are:
Motivation: Recent advances in neuroscience have shown that the human brain achieves efficient reward-based learning by integrating spiking neurons with spatial-temporal dynamics and network topologies with biologically-plausible connectivity patterns. This integration allows spiking neurons to efficiently combine information across and within layers, enhancing the network's information processing ability.
Approach: The BPT-SAN incorporates spiking neurons with intricate spatial-temporal dynamics and introduces intra-layer connections, enhancing spatial-temporal state representation and facilitating more precise biological simulations. It models the local nonlinearities of dendritic trees within the inter-layer connections and introduces lateral interactions between adjacent neurons in the intra-layer connections.
Hybrid Learning: The BPT-SAN is trained in conjunction with artificial critic networks using the TD3 and SAC policy-based DRL algorithms within a hybrid learning framework.
Evaluation: The BPT-SAN is comprehensively evaluated on four continuous control tasks from OpenAI Gym, including Hopper-v3, Walker2d-v3, Half-Cheetah-v3, and Ant-v3. The results show that the BPT-SAN outperforms its artificial actor network counterpart and the regular spiking actor network across all tasks.
Ablation Study: The authors conduct an ablation study to demonstrate the importance of the two key network topologies (inter-layer nonlinear dendritic trees and intra-layer lateral interactions) in enhancing the BPT-SAN's performance.
Overall, the BPT-SAN represents a significant advancement towards integrating biologically-plausible principles into deep reinforcement learning, leading to improved decision-making capabilities.
Stats
The content does not contain any explicit numerical data or metrics. It focuses on describing the proposed BPT-SAN architecture and its performance evaluation on various continuous control tasks.
Quotes
The content does not contain any striking quotes that support the key logics. It is primarily a technical description of the proposed method.