This paper reports the first stable partial-observability training on Humanoid-v4, using a parallel history encoder that matches full-state TD3 performance in most tested state-removal settings.
Bellman, Dynamic Programming (Princeton University Press, Princeton, NJ) (1957), intro- duces the formalism of Markov decision processes (MDPs) and the principle of optimality
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Success in Humanoid Reinforcement Learning under Partial Observation
This paper reports the first stable partial-observability training on Humanoid-v4, using a parallel history encoder that matches full-state TD3 performance in most tested state-removal settings.