scramble-generator
DeepCubeA
| scramble-generator | DeepCubeA | |
|---|---|---|
| 1 | 1 | |
| 1 | 141 | |
| - | - | |
| 9.4 | 5.2 | |
| 15 days ago | 9 months ago | |
| Python | Python | |
| GNU General Public License v3.0 only | - |
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scramble-generator
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.
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.
PPO-PyTorch - Minimal implementation of clipped objective Proximal Policy Optimization (PPO) in PyTorch
min2phase - Rubik's Cube Solver. An optimized implementation of Kociemba's two-phase algorithm.
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.
minimalRL - Implementations of basic RL algorithms with minimal lines of codes! (pytorch based)
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).
muzero-general - MuZero
cleanrl - High-quality single file implementation of Deep Reinforcement Learning algorithms with research-friendly features (PPO, DQN, C51, DDPG, TD3, SAC, PPG)