DeepCubeA
minimalRL
| DeepCubeA | minimalRL | |
|---|---|---|
| 1 | 5 | |
| 141 | 2,725 | |
| - | - | |
| 5.2 | 1.6 | |
| 9 months ago | about 1 year ago | |
| Python | Python | |
| - | MIT License |
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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.
minimalRL
- Does anyone know good python sources hardcoded of RL?
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Question about pseudocodes
Did you try minimalRL?
- Rl algorithm implemented
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RL agent for simple games?
This github is great.
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PPO+LSTM Implementation
Maybe this implementation helps: https://github.com/seungeunrho/minimalRL/blob/master/ppo-lstm.py
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.
ElegantRL - Massively Parallel Deep Reinforcement Learning. 🔥
PPO-PyTorch - Minimal implementation of clipped objective Proximal Policy Optimization (PPO) in PyTorch
Pytorch-PCGrad - Pytorch reimplementation for "Gradient Surgery for Multi-Task Learning"
min2phase - Rubik's Cube Solver. An optimized implementation of Kociemba's two-phase algorithm.
DeepRL-TensorFlow2 - 🐋 Simple implementations of various popular Deep Reinforcement Learning algorithms using TensorFlow2
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.
rlpyt - Reinforcement Learning in PyTorch
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).
pomdp-baselines - Simple (but often Strong) Baselines for POMDPs in PyTorch, ICML 2022
muzero-general - MuZero
deep-RL-trading - playing idealized trading games with deep reinforcement learning