envpool VS D4RL

Compare envpool vs D4RL and see what are their differences.

D4RL

A collection of reference environments for offline reinforcement learning (by Farama-Foundation)
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envpool D4RL
3 2
1,017 1,193
3.5% 4.3%
4.2 0.0
about 1 month ago 2 months ago
C++ Python
Apache License 2.0 Apache License 2.0
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.

envpool

Posts with mentions or reviews of envpool. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2022-12-20.

D4RL

Posts with mentions or reviews of D4RL. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2022-12-20.

What are some alternatives?

When comparing envpool and D4RL you can also consider the following projects:

ns3-gym - ns3-gym - The Playground for Reinforcement Learning in Networking Research

open_spiel - OpenSpiel is a collection of environments and algorithms for research in general reinforcement learning and search/planning in games.

thread-pool - BS::thread_pool: a fast, lightweight, and easy-to-use C++17 thread pool library

D4RL-Evaluations

matplotlibcpp17 - Alternative to matplotlibcpp with better syntax, based on pybind

ecole - Extensible Combinatorial Optimization Learning Environments

Taskflow - A General-purpose Parallel and Heterogeneous Task Programming System

pyTORCS-docker - Docker-based, gym-like torcs environment with vision.

ViZDoom - Reinforcement Learning environments based on the 1993 game Doom :godmode:

ConcurrentDeque - Fast, generalized, implementation of the Chase-Lev lock-free work-stealing deque for C++17

loneliless - A Deep-Q Network playing a single player Pong game. Network done in Python (Tensorflow-gpu) with the single player Pong game implemented in C++ (Openframeworks) and both binded with Pybind11.

policy-adaptation-during-deployment - Training code and evaluation benchmarks for the "Self-Supervised Policy Adaptation during Deployment" paper.