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.

Diversity in equally-performing policies.We show that different neighborhoods correspond to different post-update return distributions and agent behaviors. We discover that at equal average returns, different policies obtained by the same deep RL algorithm may in fact have substantially different distributional profiles, as measured by statistics of the post-update return distribution.

The goal of reinforcement learning algorithms is to estimate and/or optimise the value function.

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.