Biologically Plausible Real-Time Recurrent Reinforcement Learning for Partially Observable Environments

Core Concepts

A novel real-time recurrent reinforcement learning (RTRRL) approach that combines a biologically plausible Meta-RL RNN architecture, a TD(λ) actor-critic algorithm, and a random-feedback local-online (RFLO) optimization technique to solve discrete and continuous control tasks in partially observable Markov decision processes (POMDPs).

Abstract

The paper proposes a novel real-time recurrent reinforcement learning (RTRRL) approach that aims to be biologically plausible. RTRRL consists of three key components:

Meta-RL RNN Architecture: A recurrent neural network (RNN) architecture that implements an actor-critic algorithm on its own, inspired by the interplay between the dorsal and ventral striatum in the basal ganglia.

TD(λ) Actor-Critic Algorithm: An actor-critic algorithm that uses temporal difference (TD) learning and Dutch eligibility traces to train the weights of the Meta-RL network.

RFLO Optimization: A biologically plausible random-feedback local-online (RFLO) algorithm for computing the gradients of the network parameters, avoiding weight transport and ensuring locality of gradient information.

The authors compare RTRRL with popular but biologically implausible RL algorithms that use backpropagation through time (BPTT) or real-time recurrent learning (RTRL) for gradient computation. The results show that RTRRL with RFLO can outperform BPTT-based methods, especially on tasks requiring exploration in unfavorable environments. RTRRL is grounded in neuroscience and provides a model of reward-based learning in the human brain.

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Key Insights Distilled From

Real-Time Recurrent Reinforcement Learning

by Julian Lemme... at arxiv.org 03-29-2024

https://arxiv.org/pdf/2311.04830.pdf

Deeper Inquiries

How can RTRRL be extended to incorporate batched experience replay while preserving biological plausibility

To incorporate batched experience replay into RTRRL while maintaining biological plausibility, we can introduce a mechanism that allows for storing and sampling batches of experiences without violating the principles of neural network training. One approach could be to implement a form of "biologically inspired replay buffer" where experiences are stored in a way that mimics the consolidation and retrieval processes observed in the brain. This buffer could prioritize experiences based on their relevance or novelty, similar to how memory consolidation occurs during sleep in biological systems. By introducing this mechanism, RTRRL can benefit from the advantages of batched experience replay while staying true to its biologically plausible foundations.

How does the performance of RTRRL compare to other biologically plausible RL approaches, such as spiking neural networks trained with spike-time dependent plasticity

When comparing the performance of RTRRL to other biologically plausible RL approaches, such as spiking neural networks trained with spike-time dependent plasticity (STDP), several factors come into play. RTRRL offers a unique combination of real-time recurrent learning, temporal difference methods, and biologically plausible optimization techniques like RFLO. While spiking neural networks trained with STDP have shown promise in mimicking certain aspects of neural computation, they may face challenges in handling continuous control tasks and complex environments due to the discrete nature of spiking events. RTRRL, on the other hand, leverages continuous-time recurrent neural networks and eligibility traces to efficiently learn and adapt in partially observable environments. The performance comparison between RTRRL and spiking neural networks trained with STDP would depend on the specific task requirements, computational resources, and the level of biological fidelity desired in the model.

What are the potential implications of RTRRL for understanding the neural mechanisms underlying reinforcement learning in the brain

The potential implications of RTRRL for understanding the neural mechanisms underlying reinforcement learning in the brain are significant. By modeling reward-based learning processes in a biologically plausible manner, RTRRL provides insights into how neural networks in the brain might operate during decision-making and learning tasks. Specifically, RTRRL's utilization of a Meta-RL architecture, TD(λ) actor-critic algorithm, and RFLO optimization aligns with known neural mechanisms involving reward prediction errors, dopaminergic pathways, and synaptic plasticity. The ability of RTRRL to solve discrete and continuous control tasks in partially observable environments while mimicking key aspects of neural computation suggests that it could serve as a valuable tool for studying the neural basis of reinforcement learning. Furthermore, RTRRL's performance in tasks requiring exploration, memory capacity, and deep reinforcement learning highlights its potential to bridge the gap between artificial and biological intelligence, shedding light on how the brain processes rewards and adapts behavior in dynamic environments.