policy-adaptation-during-deployment
es_pytorch
| policy-adaptation-during-deployment | es_pytorch | |
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| 1 | 1 | |
| 109 | 23 | |
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| 1.8 | 0.0 | |
| over 3 years ago | over 2 years ago | |
| Python | Python | |
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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
es_pytorch
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What is the greatest achievement of Genetic Algorithms[D]?
ES, specifically OpenAI's ES (and to an extent CMA-ES). This has been shown to be very competitive with modern state of the art RL algorithms. A huge benefit of it is that it's incredibly easy to implement (I'm gonna shamelessly plug my implementation if you want to see the inner workings)
What are some alternatives?
Ne2Ne-Image-Denoising - Deep Unsupervised Image Denoising, based on Neighbour2Neighbour training
muzero-general - MuZero
envpool - C++-based high-performance parallel environment execution engine (vectorized env) for general RL environments.
pureples - Pure Python Library for ES-HyperNEAT. Contains implementations of HyperNEAT and ES-HyperNEAT.
stable-baselines3 - PyTorch version of Stable Baselines, reliable implementations of reinforcement learning algorithms.
neat-python - Python implementation of the NEAT neuroevolution algorithm
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).
Super-mario-bros-PPO-pytorch - Proximal Policy Optimization (PPO) algorithm for Super Mario Bros
drl_grasping - Deep Reinforcement Learning for Robotic Grasping from Octrees
dmc2gymnasium - Gymnasium integration for the DeepMind Control (DMC) suite
drq - DrQ: Data regularized Q