autonomous-learning-library
skrl
| autonomous-learning-library | skrl | |
|---|---|---|
| 2 | 7 | |
| 639 | 410 | |
| - | - | |
| 7.6 | 8.7 | |
| 2 months ago | 6 days ago | |
| Python | Python | |
| MIT License | MIT License |
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autonomous-learning-library
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What's the best "Non-Black Box" framework for SOTA algorithms?
I find Autonomous Learning Library well-designed and clean, despite its modularity to some degree.
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Where do people get their algorithm implementations from?
I very strongly recommend the autonomous learning library: https://github.com/cpnota/autonomous-learning-library
skrl
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Isaac Gym with Off-policy Algorithms
skrl will allow you to easily configure and use off-policy algorithms such as DDPG, TD3 and SAC in Isaac Gym, Omniverse Isaac Gym and Isaac Orbit, but I think there will not be significant gains compared to on-policy algorithms.
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Choosing a framework in 2023
Check its comprehensive documentation at https://skrl.readthedocs.io
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Best recurrent RL library?
Also, skrl. It supports RNN, LSTM, GRU, and other variants for A2C, DDPG, PPO, SAC, TD3, and TRPO agents. See the models basic usage and examples
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What is the limit on parallel environments?
In this case, I encourage you to try the skrl RL library that fully supports all of them, among others.
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What's the best "Non-Black Box" framework for SOTA algorithms?
I encourage you to try skrl (https://skrl.readthedocs.io).
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I have a PPO implementation but I am pretty sure it wrong. I need this correct because I would like to add LSTM layer over this. Could someone have a look?
I encourage you to take a look at the skrl library...
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Can we use RNN in RL?
This is the list of examples (to be included in the documentation) that includes RNN: (ddpg_gym_pendulumnovel_gru.py, ddpg_gym_pendulumnovel_lstm.py, ddpg_gym_pendulumnovel_rnn.py, etc.)... and here are some RNN benchmarking results (to be updated for the release)
What are some alternatives?
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).
IsaacGymEnvs - Isaac Gym Reinforcement Learning Environments
PPO-PyTorch - Minimal implementation of clipped objective Proximal Policy Optimization (PPO) in PyTorch
awesome-isaac-gym - A curated list of awesome NVIDIA Issac Gym frameworks, papers, software, and resources
deep_rl_zoo - A collection of Deep Reinforcement Learning algorithms implemented with PyTorch to solve Atari games and classic control tasks like CartPole, LunarLander, and MountainCar.
pfrl - PFRL: a PyTorch-based deep reinforcement learning library
learning-to-drive-in-5-minutes - Implementation of reinforcement learning approach to make a car learn to drive smoothly in minutes
OmniIsaacGymEnvs - Reinforcement Learning Environments for Omniverse Isaac Gym
Tetris-deep-Q-learning-pytorch - Deep Q-learning for playing tetris game
pomdp-baselines - Simple (but often Strong) Baselines for POMDPs in PyTorch, ICML 2022
Meta-SAC - Auto-tune the Entropy Temperature of Soft Actor-Critic via Metagradient - 7th ICML AutoML workshop 2020
cleanrl - High-quality single file implementation of Deep Reinforcement Learning algorithms with research-friendly features (PPO, DQN, C51, DDPG, TD3, SAC, PPG)