Comments (1)
I have a similar issue. When the reward is large, the loss from the value function is huge compared to the policy loss, and training is unstable. One way to solve it is to rescale reward. It breaks when reward in the later time steps are several magnitude difference than the early time steps.
Another solution is to normalize the value function. You may check out this repo. It may work or break as documented in What Matters In On-Policy Reinforcement Learning? A Large-Scale Empirical Study
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