garage
metaworld
garage | metaworld | |
---|---|---|
5 | 2 | |
1,813 | 829 | |
0.4% | - | |
0.0 | 3.5 | |
about 1 year ago | over 1 year ago | |
Python | Python | |
MIT License | MIT License |
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garage
- Are there any follow-up studies of RL^2 algorithms?
- Which python library to pick for RL as a beginner
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Actor-critic reinforce-style gradient with entropy regularization vs soft actor-critic
max: https://github.com/rlworkgroup/garage/blob/62bbc5cec70480e3bf2039cea7f130befecbef10/src/garage/torch/algos/vpg.py#L158 regularized: https://github.com/rlworkgroup/garage/blob/62bbc5cec70480e3bf2039cea7f130befecbef10/src/garage/torch/algos/vpg.py#L343
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Do you have any recommendation on a Reinforcement Learning library for Python?
TF-Agents and Garage look interesting and would be my first stop. Unfortunately I picked OpenAI baselines (not stable baselines) but it isn't supported any more.
- How to do unit testing for reinforcement learning
metaworld
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Are there any follow-up studies of RL^2 algorithms?
Hi r/reinforcementlearning! I recently started to be interested in meta-reinforcement learning, and I am particularly interested in models using recurrent neural networks such as RL2. But after few search I found that most of the recent approach for meta-reinforcement learning is based on MARL method, Although RL2 performed very well in meta rl benchmark paper, meta-world. And it was hard to find follow-up research of RL2 at the same time. Does anyone knows about follow-up researches of RL2?
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[D] Creating benchmarks for reinforcement learning
How long does it take to write a benchmark for RL like meta-world (https://github.com/rlworkgroup/metaworld) or multiagent emergence environments (https://github.com/openai/multi-agent-emergence-environments)?
What are some alternatives?
lightning-hydra-template - PyTorch Lightning + Hydra. A very user-friendly template for ML experimentation. ⚡🔥⚡
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).
Metaworld - Collections of robotics environments geared towards benchmarking multi-task and meta reinforcement learning
tianshou - An elegant PyTorch deep reinforcement learning library.
VMAgent - Our VMAgent is a platform for exploiting Reinforcement Learning (RL) on Virtual Machine (VM) scheduling tasks.
multi-agent-emergence-environments - Environment generation code for the paper "Emergent Tool Use From Multi-Agent Autocurricula"
nn-template - Generic template to bootstrap your PyTorch project.
rl_lib - Series of deep reinforcement learning algorithms 🤖
agents - TF-Agents: A reliable, scalable and easy to use TensorFlow library for Contextual Bandits and Reinforcement Learning.
d2l-en - Interactive deep learning book with multi-framework code, math, and discussions. Adopted at 500 universities from 70 countries including Stanford, MIT, Harvard, and Cambridge.