N-step returns #282
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N-step returns #282
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Theory
This change replacing the Bellman operator with an N-step variant. N-step returns are widely used in the context of many policy gradient algorithms as well as Q-learning variants. Using N-step returns often lead to faster learning.
I tested it on BipedalWalker-v3 with 1, 5 and 10 steps.
Charts
Math
Links
Rainbow: Combining Improvements in Deep Reinforcement Learning (Multi-step learning)