policy-adaptation-during-deployment
minimalRL
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| policy-adaptation-during-deployment | minimalRL | |
|---|---|---|
| 1 | 5 | |
| 109 | 2,725 | |
| - | - | |
| 1.8 | 1.6 | |
| over 3 years ago | about 1 year ago | |
| Python | Python | |
| - | MIT License |
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policy-adaptation-during-deployment
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Exploring Self-Supervised Policy Adaptation To Continue Training After Deployment Without Using Any Rewards
Code: https://github.com/nicklashansen/policy-adaptation-during-deployment
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?
Ne2Ne-Image-Denoising - Deep Unsupervised Image Denoising, based on Neighbour2Neighbour training
ElegantRL - Massively Parallel Deep Reinforcement Learning. 🔥
envpool - C++-based high-performance parallel environment execution engine (vectorized env) for general RL environments.
Pytorch-PCGrad - Pytorch reimplementation for "Gradient Surgery for Multi-Task Learning"
stable-baselines3 - PyTorch version of Stable Baselines, reliable implementations of reinforcement learning algorithms.
DeepRL-TensorFlow2 - 🐋 Simple implementations of various popular Deep Reinforcement Learning algorithms using TensorFlow2
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).
rlpyt - Reinforcement Learning in PyTorch
drl_grasping - Deep Reinforcement Learning for Robotic Grasping from Octrees
pomdp-baselines - Simple (but often Strong) Baselines for POMDPs in PyTorch, ICML 2022
dmc2gymnasium - Gymnasium integration for the DeepMind Control (DMC) suite
deep-RL-trading - playing idealized trading games with deep reinforcement learning