Pbo Alternatives

Similar projects and alternatives to pbo based on common topics and language

ppocma

1 66 10.0 Python pbo VS ppocma
nes-torch

3 17 3.6 Python pbo VS nes-torch

Minimal PyTorch Library for Natural Evolution Strategies
InfluxDB

www.influxdata.com featured

Power Real-Time Data Analytics at Scale. Get real-time insights from all types of time series data with InfluxDB. Ingest, query, and analyze billions of data points in real-time with unbounded cardinality.
Gradient-Free-Optimizers

11 1,114 5.0 Python pbo VS Gradient-Free-Optimizers

Simple and reliable optimization with local, global, population-based and sequential techniques in numerical discrete search spaces.
SpaceDrones

1 4 7.8 Python pbo VS SpaceDrones

A simple learning environment with space drones for evolution-inspired optimization.
RocketLander

1 1 1.7 Python pbo VS RocketLander

A simple framework equipped with optimization algorithms, such as reinforcement learning, evolution strategies, genetic optimization, and simulated annealing, to enable an orbital rocket booster to land autonomously.
tianshou

8 7,459 9.5 Python pbo VS tianshou

An elegant PyTorch deep reinforcement learning library.
PPO-PyTorch

2 1,493 2.8 Python pbo VS PPO-PyTorch

Minimal implementation of clipped objective Proximal Policy Optimization (PPO) in PyTorch
SaaSHub

www.saashub.com featured

SaaSHub - Software Alternatives and Reviews. SaaSHub helps you find the best software and product alternatives

NOTE: The number of mentions on this list indicates mentions on common posts plus user suggested alternatives. Hence, a higher number means a better pbo alternative or higher similarity.

Suggest an alternative to pbo

pbo reviews and mentions

Posts with mentions or reviews of pbo. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2022-09-27.

Why we use diagonal gaussian rather than multivariate guassian (with full covariance matrix)
2 projects | /r/reinforcementlearning | 27 Sep 2022

I have implemented full covariance matrix output from neural networks in a drl-related project, in which I devised an optimization technique out of mixing a PG approach with CMA-ES concepts, and you can find a possible implementation of how to do so here: https://github.com/jviquerat/pbo In this specific context, full covariance matrix gave a very significant performance boost. Yet I don't want to draw premature conclusions on whether this will end up the same when plugged into PPO.