Evolving Hierarchical Memory-Prediction Machines in Multi-Task
Reinforcement Learning
- URL: http://arxiv.org/abs/2106.12659v1
- Date: Wed, 23 Jun 2021 21:34:32 GMT
- Title: Evolving Hierarchical Memory-Prediction Machines in Multi-Task
Reinforcement Learning
- Authors: Stephen Kelly, Tatiana Voegerl, Wolfgang Banzhaf, Cedric Gondro
- Abstract summary: Behavioural agents must generalize across a variety of environments and objectives over time.
We use genetic programming to evolve highly-generalized agents capable of operating in six unique environments from the control literature.
We show that emergent hierarchical structure in the evolving programs leads to multi-task agents that succeed by performing a temporal decomposition and encoding of the problem environments in memory.
- Score: 4.030910640265943
- License: http://creativecommons.org/licenses/by-nc-sa/4.0/
- Abstract: A fundamental aspect of behaviour is the ability to encode salient features
of experience in memory and use these memories, in combination with current
sensory information, to predict the best action for each situation such that
long-term objectives are maximized. The world is highly dynamic, and
behavioural agents must generalize across a variety of environments and
objectives over time. This scenario can be modeled as a partially-observable
multi-task reinforcement learning problem. We use genetic programming to evolve
highly-generalized agents capable of operating in six unique environments from
the control literature, including OpenAI's entire Classic Control suite. This
requires the agent to support discrete and continuous actions simultaneously.
No task-identification sensor inputs are provided, thus agents must identify
tasks from the dynamics of state variables alone and define control policies
for each task. We show that emergent hierarchical structure in the evolving
programs leads to multi-task agents that succeed by performing a temporal
decomposition and encoding of the problem environments in memory. The resulting
agents are competitive with task-specific agents in all six environments.
Furthermore, the hierarchical structure of programs allows for dynamic run-time
complexity, which results in relatively efficient operation.
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