ai-traineree
machin
| ai-traineree | machin | |
|---|---|---|
| 1 | 2 | |
| 24 | 381 | |
| - | - | |
| 0.0 | 1.8 | |
| about 2 years ago | over 2 years ago | |
| Python | Python | |
| Apache License 2.0 | MIT License |
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ai-traineree
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Rainbow Library
Maybe you'll find this easy to use https://github.com/laszukdawid/ai-traineree
machin
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Best PyTorch RL library for doing research
Machin is really nice, it is very easy to use and to try different things, although it’s developed by one person and maybe not appropriately tested yet.
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Is there a consensus about RL frameworks?
I found this repo very helpful to get started: https://github.com/iffiX/machin
What are some alternatives?
dopamine - Dopamine is a research framework for fast prototyping of reinforcement learning algorithms.
stable-baselines3 - PyTorch version of Stable Baselines, reliable implementations of reinforcement learning algorithms.
action-branching-agents - (AAAI 2018) Action Branching Architectures for Deep Reinforcement Learning
cleanrl - High-quality single file implementation of Deep Reinforcement Learning algorithms with research-friendly features (PPO, DQN, C51, DDPG, TD3, SAC, PPG)
autonomous-learning-library - A PyTorch library for building deep reinforcement learning agents.
Apache Impala - Apache Impala
DQN-Atari - Deep Q-Learning (DQN) implementation for Atari pong.
RL-Adventure - Pytorch Implementation of DQN / DDQN / Prioritized replay/ noisy networks/ distributional values/ Rainbow/ hierarchical RL
ElegantRL - Massively Parallel Deep Reinforcement Learning. 🔥
tianshou - An elegant PyTorch deep reinforcement learning library.
seed_rl - SEED RL: Scalable and Efficient Deep-RL with Accelerated Central Inference. Implements IMPALA and R2D2 algorithms in TF2 with SEED's architecture.