DREAM Architecture: a Developmental Approach to Open-Ended Learning in Robotics

By Stéphane Doncieux, Nicolas Bredèche, L. L. Goff, Benoît Girard, Alexandre Coninx, Olivier Sigaud, Mehdi Khamassi, Natalia Díaz Rodríguez, David Filliat, Timothy M. Hospedales and 2 more. 2020

19 citations recorded by the source corpus.

Robots are still limited to controlled conditions, that the robot designer knows with enough details to endow the robot with the appropriate models or behaviors. Learning algorithms add some flexibility with the ability to discover the appropriate behavior given either some demonstrations or a reward to guide its exploration with a reinforcement learning algorithm. Reinforcement learning algorithms rely on the definition of state and action spaces that define reachable behaviors. Their adaptation capability critically depends on the representations of these spaces: small and discrete spaces result in fast learning while large and continuous spaces are challenging and either require a long training period or prevent the robot from converging to an appropriate behavior. Beside the operational cycle of policy execution and the learning cycle, which works at a slower time scale to acquire new policies, we introduce the redescription cycle, a third cycle working at an even slower time scale to generate or adapt the required representations to the robot, its environment and the task. We introduce the challenges raised by this cycle and we present DREAM (Deferred Restructuring of Experience in Autonomous Machines), a developmental cognitive architecture to bootstrap this redescription process stage by stage, build new state representations with appropriate motivations, and transfer the acquired knowledge across domains or tasks or even across robots. We describe results obtained so far with this approach and end up with a discussion of the questions it raises in Neuroscience.

Subfields: Artificial Intelligence, Cognitive Neuroscience

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Citations in the corpus

Towards Continual Reinforcement Learning: A Review and Perspectives (2020, background): “For example, these settings may experience a changing state and action space over the course of an agent’s lifetime while continuously generating creative behaviors (Doncieux et al., 2020).”
Acquisition of chess knowledge in AlphaZero (2021, background): “Representation learning in RL agents [45, 46, 47, 48, 49, 50] can be beneficial when used to learn low-dimensional representations for states, policies and actions, hoping to meaningfully capture the variations in agent’s environment and disentangle the contributing factors.”