DeepCubeA
cleanrl
| DeepCubeA | cleanrl | |
|---|---|---|
| 1 | 41 | |
| 141 | 4,529 | |
| - | - | |
| 5.2 | 6.3 | |
| 9 months ago | 21 days ago | |
| Python | Python | |
| - | GNU General Public License v3.0 or later |
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DeepCubeA
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DeepRL and Rubik’s Cube
We are looking at Rubik’s Cube as target problem, and kicking off a project which will start from https://github.com/forestagostinelli/DeepCubeA and go from there.
cleanrl
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[P] PettingZoo 1.24.0 has been released (including Stable-Baselines3 tutorials)
PettingZoo 1.24.0 is now live! This release includes Python 3.11 support, updated Chess and Hanabi environment versions, and many bugfixes, documentation updates and testing expansions. We are also very excited to announce 3 tutorials using Stable-Baselines3, and a full training script using CleanRL with TensorBoard and WandB.
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PPO agent for "2048": help requested
Here's where the problem starts: after implementing a custom environment that follows the typical gymnasium interface, and use a slightly adjusted PPO implementation from CleanRL, I cannot get the agent to learn anything at all, even though this specific implementation seems to work just fine on basic gymnasium examples. I am hoping the RL community here can help me with some useful pointers.
- [P] 10x faster reinforcement learning hyperparameter optimization than SOTA - now with distributed training!
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PPO ignores high rewards in deterministic sytem
Try out a standard implementation with some standard parameters from here: https://github.com/vwxyzjn/cleanrl/tree/master/cleanrl
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SB3 - NotImplementedError: Box([-1. -1. -8.], [1. 1. 8.], (3,), <class 'numpy.float32'>) observation space is not supported
I am trying to run cleanrl on the `Pendulum-v1` environment. I did that by going here and changing the default `env-id` to ` parser.add_argument("--env-id", type=str, default="Pendulum-v1",
- Cartpole and mountain car
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cleanrl gym issues
git clone https://github.com/vwxyzjn/cleanrl.git && cd cleanrl poetry install
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Why is my Soft Actor Critic Algorithm not learning?
Can someone please help me debug my implementation of SAC. Please let me know if you have any questions. I tried comparing my work with CleanRL and caught a couple of errors. However, my implementation does diverge a lot from theirs as I wanted to test my understanding.
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Model-based hierarchical reinforcement learning
Shameless self-plug: as far as implementation is concerned, I am working on a (hopefully) easier to understand Dreamer architecture under the CleanRL library, toward also re-implementing Director, Dreamer-v3, and and JAX variant for faster training.
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[P] Robust Policy Optimization is now in CleanRL 🔥!
Happy to share that CleanRL now has a new algorithm called Robust Policy Optimization — 5 lines of code change to PPO to get better performance in 57 out of 61 continuous action envs 🚀 (e.g., dm_control)
What are some alternatives?
Muzero - Pytorch Implementation of MuZero for gym environment. It support any Discrete , Box and Box2D configuration for the action space and observation space.
stable-baselines3 - PyTorch version of Stable Baselines, reliable implementations of reinforcement learning algorithms.
PPO-PyTorch - Minimal implementation of clipped objective Proximal Policy Optimization (PPO) in PyTorch
tianshou - An elegant PyTorch deep reinforcement learning library.
min2phase - Rubik's Cube Solver. An optimized implementation of Kociemba's two-phase algorithm.
d3rlpy - An offline deep reinforcement learning library
Muzero-unplugged - Pytorch Implementation of MuZero Unplugged for gym environment. This algorithm is capable of supporting a wide range of action and observation spaces, including both discrete and continuous variations.
reinforcement-learning-discord-wiki - The RL discord wiki
minimalRL - Implementations of basic RL algorithms with minimal lines of codes! (pytorch based)
mbrl-lib - Library for Model Based RL
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).
machin - Reinforcement learning library(framework) designed for PyTorch, implements DQN, DDPG, A2C, PPO, SAC, MADDPG, A3C, APEX, IMPALA ...