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A survey of progress on cooperative multi-agent reinforcement learning in open environment
Tesseract: Tensorised actors for multi-agent reinforcement learning
Reinforcement Learning in large action spaces is a challenging problem. This is especially
true for cooperative multi-agent reinforcement learning (MARL), which often requires …
true for cooperative multi-agent reinforcement learning (MARL), which often requires …
Semantically aligned task decomposition in multi-agent reinforcement learning
The difficulty of appropriately assigning credit is particularly heightened in cooperative
MARL with sparse reward, due to the concurrent time and structural scales involved …
MARL with sparse reward, due to the concurrent time and structural scales involved …
Regularized softmax deep multi-agent q-learning
Tackling overestimation in $ Q $-learning is an important problem that has been extensively
studied in single-agent reinforcement learning, but has received comparatively little attention …
studied in single-agent reinforcement learning, but has received comparatively little attention …
Combining behaviors with the successor features keyboard
Abstract The Option Keyboard (OK) was recently proposed as a method for transferring
behavioral knowledge across tasks. OK transfers knowledge by adaptively combining …
behavioral knowledge across tasks. OK transfers knowledge by adaptively combining …