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Spiking Neural Networks for Continuous Control: Neuromorphic Reinforcement Learning in Conventional Computing
Jessica Hunter, Md Maruf Hossain Shuvo, Krishna Roy
TL;DR
Continuous-control reinforcement learning remains under-validated on neuromorphic hardware, particularly regarding whether a spiking actor changes performance before hardware benefits appear. The paper introduces SANSAC, a minimal SAC variant with an SNN actor, and finds statistically indistinguishable performance from SAC across hidden dimensions on conventional hardware, while identifying important scope and hardware-validation limits.
Problem
Neuromorphic reinforcement learning for continuous control lacks validation of whether replacing a conventional SAC actor with an SNN affects agent performance.
Method
SANSAC replaces only SAC’s actor with an SNN and compares it with SAC on identical conventional hardware across multiple hidden dimensions.
Results
SANSAC achieves performance statistically indistinguishable from SAC across multiple hidden-dimension configurations.
Takeaways & Limitations
The results support the viability of SNN-based reinforcement learning for complex continuous-control tasks and provide a baseline for future neuromorphic implementations.
Takeaways & Limitations
The evaluation was primarily optimized and tested in Bipedal Walker, so generalizability to other continuous environments remains to be validated.
Abstract
from arXiv · showhide
Reinforcement learning (RL) algorithms have made strides over the past decade applying them to a wide range of problems and control tasks. However, the deployment of RL on neuromorphic hardware for continuous control tasks remains under-validated. Namely it is unclear whether replacing a conventional actor network with a spiking neural network (SNN) affects the performance of an agent before any hardware-specific benefits manifest. We provide a systematic validation of a minimal, neuromorphically viable spiking actor variant of Soft Actor-Critic (SAC) on conventional hardware, establishing a baseline for future neuromorphic RL research. In this paper, we propose the Spiking Actor Network Soft Actor Critic (SANSAC) to address the use of RL frameworks in continuous environments, designed as a framework that can be implemented on neuromorphic hardware. We compare a traditional Soft Actor Critic (SAC) network to SANSAC in a traditional computer. We demonstrate the near equivalent performance of SANSAC and SAC, while addressing the impact of hidden dimensions. Our results demonstrate the viability of SNN based algorithms in complex continuous environments, as well as competitive performance to traditional neural networks in traditional computers, providing a basis to continue exploring the use of SNNs in continuous RL frameworks.
1 INTRODUCTION
The paper addresses limited validation of neuromorphic reinforcement learning for continuous control by systematically comparing a minimal spiking-actor SAC variant with conventional SAC on conventional hardware.
- Motivation: Continuous-control RL remains difficult to implement reproducibly, stably, and portably on neuromorphic hardware.The paper highlights additional complexity in tasks such as robotics and POMDPs.
- Motivation: Neuromorphic hardware may reduce energy cost, training time, and hardware requirements through parallel computation.The stated benefits arise from the parallel nature of neuromorphic chips.
- Background: SNN research has expanded methods for applying biologically inspired networks to continuous-control problems.The introduction identifies SNNs as an increasing focus for advancing continuous-control solutions.
- Research gap: The paper identifies limited evidence comparing SNN and traditional neural networks for RL on conventional computers.This comparison is motivated partly by the difficulty of acquiring neuromorphic hardware.
- Approach: SANSAC replaces only SAC’s actor with an SNN and compares it with SAC across identical conventional hardware and multiple network sizes.The study also analyzes hidden-dimension effects and performance differences across several metrics.
2 RELATED WORK
The related work situates SANSAC within neuromorphic computing and biologically inspired SNN methods, including population encoding for actor-critic reinforcement learning.
- RL with SNNs: Population encoding and decoding provide a general way to add spiking representations to traditional actor-critic algorithms in continuous-control environments.The paper identifies PopSAN as a prominent framework for this approach.
- Neuromorphic hardware: Neuromorphic hardware uses analog, digital, or mixed-signal circuits to implement neurons and synapses through spike events.These systems are distinguished from conventional computers by their event-driven operation.
3 METHODS
The methods retain SAC’s continuous-control framework while replacing its actor with a neuromorphic-compatible SNN, using surrogate-gradient training and continuous-valued output decoding.
- Notation: The continuous-control setting is represented by the MDP tuple (s_t, a_t, r_t, s_t+1), with π(a|s) denoting the policy.States, actions, and rewards are indexed by time step.
- SAC: SAC uses a stochastic policy and maximum-entropy objective for complex continuous environments.The paper evaluates this established framework in Bipedal Walker and defines stability as consistent policy improvement without training collapse.
- SAC: The SAC actor transformation is rewritten into a numerically more stable form to reduce instability from tanh and Gaussian sampling.The derivation uses softplus for numerical stability during backpropagation.
- SANSAC: SANSAC keeps SAC’s critics and replay buffer while implementing the actor as a spiking neural network.The configuration is a hybrid framework intended to support a fully neuromorphic deployable agent.
- SANSAC: The SANSAC actor uses LIF neurons for hidden layers and a nonspiking LI output layer to decode continuous actions.LI neurons provide constant voltage outputs instead of spike trains at the final layer.
- Training: SANSAC trains with surrogate gradients and BPTT, while retaining SAC loss calculations apart from the surrogate-gradient mechanism.The implementation uses a sigmoid surrogate gradient and an unrolled-network training procedure.
4 EXPERIMENTS
The experiments compare SAC and SANSAC across hidden dimensions in Gymnasium Bipedal Walker using matched seeds and identical early-stopping criteria. SANSAC performs similarly to SAC across configurations, but lower dimensions reduce performance and SANSAC requires longer conventional-computer training.
- Experimental setup: SAC and SANSAC use matched hidden-dimension configurations, synchronized random seeds, and identical early stopping after 100 consecutive episodes without reward improvement.Both agents were trained for up to 1200 episodes, with a minimum of 50 episodes before early stopping.
- Algorithm comparison: SANSAC and SAC show no statistically significant performance differences in any hidden-dimension configuration according to Mann-Whitney U tests.Table 2 reports negligible-to-small Cohen’s d effect sizes across configurations.
- Learning dynamics: Reward and actor-loss curves show similar learning dynamics across algorithms, although SANSAC exhibits a reward drop relative to SAC in one configuration.Curves summarize means and ±1 standard deviation across 10 random seeds.
- Algorithm comparison: Both algorithms achieve only 10% success at the lowest hidden dimension, while increasing neuron counts substantially improves performance and makes SANSAC competitive with SAC.The results attribute degradation at smaller topologies to reduced learning capacity.
- Training cost: SANSAC takes 2x the program time of SAC for the same number of episodes in tested configurations on conventional hardware.The paper attributes this bottleneck to temporal unrolling of spiking neuron dynamics on sequential architectures.
5 LIMITATIONS
The evaluation is primarily limited by its Bipedal Walker focus, the computational overhead of simulating SANSAC conventionally, and unresolved sources of performance variance and overhead.
- The comparison was primarily optimized and tested on Bipedal Walker, so generalizability to other continuous environments remains unvalidated.
- SANSAC requires 2x the program time of SAC for the same number of episodes in tested configurations.The authors attribute this behavior to temporal unrolling and its bottleneck on conventional hardware.
- The study cannot quantify how much of the observed overhead is fundamental to SANSAC versus implementation-specific inefficiency.
- SANSAC shows higher variance in success rates, potentially reflecting spiking-neuron stochasticity or surrogate-gradient approximation.The higher variance can delay convergence detection through early stopping and may affect scalability to larger problems.
6 CONCLUSION
SANSAC bridges neuromorphic design and reinforcement learning by replacing SAC’s actor with a spiking variant while preserving comparable performance across hidden dimensions. Its conventional-hardware simulation incurs overhead, but the design supports future neuromorphic implementations.
- SANSAC achieves performance statistically indistinguishable from SAC across multiple hidden dimension configurations.
- SANSAC is designed to bridge neuromorphic hardware and reinforcement learning while isolating behaviors relevant before hardware deployment.
- SANSAC’s computational overhead during conventional simulation is presented as a trade-off for compatibility with neuromorphic hardware architectures.The paper associates that hardware compatibility with potential energy-efficiency benefits.
- The results provide a foundation for future neuromorphic reinforcement learning by showing competitive performance during conventional-computer development and training.
7 FUTURE WORK
Future work should test SANSAC beyond the current neuron and encoding choices, evaluate physical or FPGA implementations, and examine scalability and hybrid training strategies.
- Future studies should rigorously test neuron types and encoding and decoding behaviors beyond the current implementation.The current implementation uses LIF neurons and Sigmoid surrogate gradients.
- Implementing similar algorithms on physical neuromorphic hardware, including FPGAs, could make the research more practical and accessible.The paper notes that many neuromorphic chips remain experimental and difficult to acquire.
- Further investigation should assess SANSAC’s scalability in more complex environments and larger network architectures.This would provide insight into practical limitations and advantages.
- Hybrid training strategies using conventional and neuromorphic hardware at different learning phases could help mitigate observed computational overhead.
A.1 HYPERPARAMETERS
The supplied appendix material identifies a hyperparameter table and records that all data were gathered on an Nvidia GeForce RTX 4070.
- Table A.1 is identified as the hyperparameter table.
- All data were gathered using an Nvidia GeForce RTX 4070.
A.2 POLICY LOG-PROBABILITY DERIVATION
The policy log-probability derivation replaces unstable tanh operations with softplus while preserving differentiability for continuous-control policy optimization.
- The derivation begins with the original SAC policy log-probability.
- Softplus replaces unstable tanh operations in the transformed policy log-probability.The paper defines softplus(x) ≜ log(1 + e^x).
- The transformation is intended to ensure gradient stability during policy-network backpropagation while avoiding vanishing gradients.It maintains differentiability for continuous-control environments.