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Found 10 papers, 4 papers with code
Structurally Flexible Neural Networks: Evolving the Building Blocks for General Agents
Artificial neural networks used for reinforcement learning are structurally rigid, meaning that each optimized parameter of the network is tied to its specific placement in the network structure.
Evolving Reservoirs for Meta Reinforcement Learning
At the developmental scale, we employ these evolved reservoirs to facilitate the learning of a behavioral policy through Reinforcement Learning (RL).
Emergence of Collective Open-Ended Exploration from Decentralized Meta-Reinforcement Learning
We further find that the agents learned collective exploration strategies extend to an open ended task setting, allowing them to solve task trees of twice the depth compared to the ones seen during training.
Dynamics of niche construction in adaptable populations evolving in diverse environments
In this work, we study NC in simulation environments that consist of multiple, diverse niches and populations that evolve their plasticity, evolvability and niche-constructing behaviors.
Eco-evolutionary Dynamics of Non-episodic Neuroevolution in Large Multi-agent Environments
Neuroevolution (NE) has recently proven a competitive alternative to learning by gradient descent in reinforcement learning tasks.
Social Network Structure Shapes Innovation: Experience-sharing in RL with SAPIENS
Comparing the level of innovation achieved by different social network structures across different tasks shows that, first, consistent with human findings, experience sharing within a dynamic structure achieves the highest level of innovation in tasks with a deceptive nature and large search spaces.
Cultural Vocal Bursts Intensity Prediction
Reinforcement Learning (RL)
Plasticity and evolvability under environmental variability: the joint role of fitness-based selection and niche-limited competition
In this work, we study the interplay between environmental dynamics and adaptation in a minimal model of the evolution of plasticity and evolvability.
Socially Supervised Representation Learning: the Role of Subjectivity in Learning Efficient Representations
In this work, we propose that aligning internal subjective representations, which naturally arise in a multi-agent setup where agents receive partial observations of the same underlying environmental state, can lead to more data-efficient representations.
Grounding Artificial Intelligence in the Origins of Human Behavior
Recent advances in Artificial Intelligence (AI) have revived the quest for agents able to acquire an open-ended repertoire of skills.
Design of Capacity-Approaching Low-Density Parity-Check Codes using Recurrent Neural Networks
We refer to our RNN architecture as Neural Density Evolution (NDE) and determine the weights of the RNN that correspond to optimal designs by minimizing a loss function that enforces the properties of asymptotically optimal design, as well as the desired structural characteristics of the code.
