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Reservoir Computing for Fast, Simplified Reinforcement Learning on Memory Tasks

Kevin McKee

TL;DR

This paper investigates whether reservoir computing, specifically Echo State Networks with fixed recurrent weights, can provide fast and reliable short-term memory for reinforcement learning on memory-dependent tasks. It compares two ESN designs to gated memory units, simple RNNs, and LSTMs across Recall Match, Multi-armed Bandit, Sequential Bandits, and Morris Water Maze tasks, showing that fixed-weight reservoirs with trainable decoders yield substantially faster training and robust performance. Local connectivity consistently delivers the fastest training, while gated recurrent units often require orders of magnitude more episodes and show greater sensitivity to hyperparameters. The findings position reservoir computing as a practical memory substrate for rapid meta-learning in RL, with potential hardware advantages and applicability to more complex, memory-intensive tasks.

Abstract

Tasks in which rewards depend upon past information not available in the current observation set can only be solved by agents that are equipped with short-term memory. Usual choices for memory modules include trainable recurrent hidden layers, often with gated memory. Reservoir computing presents an alternative, in which a recurrent layer is not trained, but rather has a set of fixed, sparse recurrent weights. The weights are scaled to produce stable dynamical behavior such that the reservoir state contains a high-dimensional, nonlinear impulse response function of the inputs. An output decoder network can then be used to map the compressive history represented by the reservoir's state to any outputs, including agent actions or predictions. In this study, we find that reservoir computing greatly simplifies and speeds up reinforcement learning on memory tasks by (1) eliminating the need for backpropagation of gradients through time, (2) presenting all recent history simultaneously to the downstream network, and (3) performing many useful and generic nonlinear computations upstream from the trained modules. In particular, these findings offer significant benefit to meta-learning that depends primarily on efficient and highly general memory systems.

Reservoir Computing for Fast, Simplified Reinforcement Learning on Memory Tasks

TL;DR

This paper investigates whether reservoir computing, specifically Echo State Networks with fixed recurrent weights, can provide fast and reliable short-term memory for reinforcement learning on memory-dependent tasks. It compares two ESN designs to gated memory units, simple RNNs, and LSTMs across Recall Match, Multi-armed Bandit, Sequential Bandits, and Morris Water Maze tasks, showing that fixed-weight reservoirs with trainable decoders yield substantially faster training and robust performance. Local connectivity consistently delivers the fastest training, while gated recurrent units often require orders of magnitude more episodes and show greater sensitivity to hyperparameters. The findings position reservoir computing as a practical memory substrate for rapid meta-learning in RL, with potential hardware advantages and applicability to more complex, memory-intensive tasks.

Abstract

Tasks in which rewards depend upon past information not available in the current observation set can only be solved by agents that are equipped with short-term memory. Usual choices for memory modules include trainable recurrent hidden layers, often with gated memory. Reservoir computing presents an alternative, in which a recurrent layer is not trained, but rather has a set of fixed, sparse recurrent weights. The weights are scaled to produce stable dynamical behavior such that the reservoir state contains a high-dimensional, nonlinear impulse response function of the inputs. An output decoder network can then be used to map the compressive history represented by the reservoir's state to any outputs, including agent actions or predictions. In this study, we find that reservoir computing greatly simplifies and speeds up reinforcement learning on memory tasks by (1) eliminating the need for backpropagation of gradients through time, (2) presenting all recent history simultaneously to the downstream network, and (3) performing many useful and generic nonlinear computations upstream from the trained modules. In particular, these findings offer significant benefit to meta-learning that depends primarily on efficient and highly general memory systems.

Paper Structure

This paper contains 12 sections, 2 figures, 1 table.

Table of Contents

  1. Recall Match
  2. Multi-armed bandit
  3. Sequential bandits
  4. Water Maze
  5. Locally connected ESN
  6. Recall Match
  7. Multi-armed bandit
  8. Morris Water Maze
  9. Sequential bandit
  10. Limitations
  11. Appendix A: Hyperparameters
  12. Appendix B: Connectivity specifications

Figures (2)

  • Figure 1: Agent design: inputs and feedbacks are passed into the recurrent module, which then feeds forward to actions and values.
  • Figure 3: Model comparisons on four memory POMDP tasks. Each model was run 8 times per task. Colored intervals show the minimum to maximum reward per time step. Lines show mean values.