GAE
A classical reinforcement learning algorithm for estimating the advantage function. It computes an exponentially weighted average of TD errors to balance bias and variance, and is widely used in implementations of policy gradient algorithms.
Timeline (last 90 days)
Waypoint-OPD can be seen as introducing 'multi-step differencing' (similar to the idea of reducing single-step estimation variance in GAE) into cross-model distillation, using intermediate checkpoints to test the stability of the deviation direction.
When the number of parallel environments expands from 10 to 1000 while buffer_size remains unchanged, the trajectory length allocated to each environment is compressed to 1/100 of the original, causing severe trajectory truncation and undermining GAE's estimation of returns.
All Facts (2)
Waypoint-OPD can be seen as introducing 'multi-step differencing' (similar to the idea of reducing single-step estimation variance in GAE) into cross-model distillation, using intermediate checkpoints to test the stability of the deviation direction.
50%UnverifiedWhen the number of parallel environments expands from 10 to 1000 while buffer_size remains unchanged, the trajectory length allocated to each environment is compressed to 1/100 of the original, causing severe trajectory truncation and undermining GAE's estimation of returns.
50%