drq VS exorl

Compare drq vs exorl and see what are their differences.

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drq exorl
1 1
398 94
- -
0.0 4.3
over 1 year ago over 2 years ago
Jupyter Notebook Python
MIT License MIT License
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.

drq

Posts with mentions or reviews of drq. We have used some of these posts to build our list of alternatives and similar projects.

exorl

Posts with mentions or reviews of exorl. We have used some of these posts to build our list of alternatives and similar projects.

What are some alternatives?

When comparing drq and exorl you can also consider the following projects:

pytorch-a2c-ppo-acktr-gail - PyTorch implementation of Advantage Actor Critic (A2C), Proximal Policy Optimization (PPO), Scalable trust-region method for deep reinforcement learning using Kronecker-factored approximation (ACKTR) and Generative Adversarial Imitation Learning (GAIL).

d3rlpy - An offline deep reinforcement learning library

muzero-general - MuZero

machin - Reinforcement learning library(framework) designed for PyTorch, implements DQN, DDPG, A2C, PPO, SAC, MADDPG, A3C, APEX, IMPALA ...

cleanrl - High-quality single file implementation of Deep Reinforcement Learning algorithms with research-friendly features (PPO, DQN, C51, DDPG, TD3, SAC, PPG)

Super-mario-bros-PPO-pytorch - Proximal Policy Optimization (PPO) algorithm for Super Mario Bros

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

Minari - A standard format for offline reinforcement learning datasets, with popular reference datasets and related utilities

Note - Easily implement parallel training and distributed training. Machine learning library. Note.neuralnetwork.tf package include Llama2, Llama3, CLIP, ViT, ConvNeXt, SwiftFormer, etc, these models built with Note are compatible with TensorFlow and can be trained with TensorFlow.

DI-engine - OpenDILab Decision AI Engine