exploration
Scalable Multi-agent Covering Option Discovery based on Kronecker Graphs
However, calculating the Laplacian spectrum can be expensive in a matrix base.
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CQM: Curriculum Reinforcement Learning with a Quantized World Model
Recent curriculum Reinforcement Learning (RL) has shown notable progress in solving complex tasks by proposing sequences of surrogate tasks. However, the previous approaches often face challenges when they generate curriculum goals in a high-dimensional space.
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Successor-Predecessor Intrinsic Exploration Changmin Y u 1,2 Neil Burgess
Exploration is essential in reinforcement learning, particularly in environments where external rewards are sparse. Here we focus on exploration with intrinsic rewards, where the agent transiently augments the external rewards with self-generated intrinsic rewards.
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