Weproveconvergence for this method under standard assumptions and demonstrate empirically that it indeed enables lowerdiscount factors forapproximate reinforcement-learning methods.

Consider An n= grad (r)+ A grad (r)+ C| 0 B @ 01 10 ... 1 C

Delusionalbias ariseswhenthe approximation architecture limits the class ofexpressible greedy policies.

The CF of a random variable (r.v.) is the Fourier transform of its probability distribution function (PDF).

Contrastingly, others have observed that larger batch sizes tend to converge to "sharper" optimization

Since the policy used to collect transitions is changing throughout learning, the replay memory contains data coming from a mixture of policies (that differ from the agent's current policy), and

In the specific case of MuZero Reanalyse it also performs a tree search rather than a single step lookaheadusedinDyna.

Given the impressive results of deepreinforcement learning, weargueforaneedtomoreclearly understand the challenges inthis setting.

CuLE is available at https://github.com/NVlabs/cule.