optimizer (Optimizer): Wrapped optimizer. first_cycle_steps (int): First cycle step size. cycle_mult(float): Cycle steps magnification. D

Conv1d --> GroupNorm --> Mish

Neural network to generate learnable exploration noise, conditioned on time embeddings and or state embeddings. \sigma(s,t) or \sigma(s)

Pseudocode can be found in https://arxiv.org/pdf/1811.02553.pdf section 9.3 (which is based on our Baselines code, haha) Motivation is th

state input

Simple multi-layer perceptron network with residual connections for benchmarking the performance of different networks. The residual layers

Vectorized environment that runs multiple environments in parallel. It uses `multiprocessing`_ processes, and pipes for communication. Parame

Exponential moving average

on-policy buffer for state inputs and cpu storage.

Vectorized environment that serially runs multiple environments. Parameters ---------- env_fns : iterable of callable Functions t

State-only critic network. V(s)

State-action double critic network. Q(s,a) or Q_i(s,a)

With ViT backbone

Distill a multistep flow model to single step stochastic policy