bark-ml
acme
| bark-ml | acme | |
|---|---|---|
| 1 | 11 | |
| 90 | 3,381 | |
| - | 0.7% | |
| 0.0 | 6.0 | |
| 3 months ago | 1 day ago | |
| Python | Python | |
| MIT License | Apache License 2.0 |
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bark-ml
acme
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Fast and hackable frameworks for RL research
I'm tired of having my 200m frames of Atari take 5 days to run with dopamine, so I'm looking for another framework to use. I haven't been able to find one that's fast and hackable, preferably distributed or with vectorized environments. Anybody have suggestions? seed-rl seems promising but is archived (and in TF2). sample-factory seems super fast but to the best of my knowledge doesn't work with replay buffers. I've been trying to get acme working but documentation is sparse and many of the features are broken.
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How much of a MuJoCo simulation or real life robot can you train on a 3090?
I'm training a few algorithms from Deepmind's acme library on some MuJoCo models and I'm wondering how long this will take to train and what it's going to do to my electric bill. Is a 3090 or two enough to train something to keep its balance, or do a task, or do I need to wait for the 8090 to come out?
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Recomendations of framework/library for MARL
Recently dm-acme also added support for multi-agent environments. Acme: https://github.com/deepmind/acme
- Have you used any good DRL library?
- Is there a way to get PPO controlled agents to move a little more gracefully?
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Worthwhile to convert custom env to be dm_env compatible?
Can anyone speak to their experience using acme (https://github.com/deepmind/acme) and by extension dm_env (https://github.com/deepmind/dm_env)? I'm wondering if it would be worthwhile for me to invest the time into converting my custom environment (which loosely follows the standard RL setup) over to this format.
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[D] Physics and Reinforcement Learning - Discussion of Deepmind's work
acme/acme/agents/tf/mpo at master · deepmind/acme · GitHub
- Applied resources in Pytorch?
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deepmind acme compatible with windows?
after installing it in a clean env, I tried to run the example provided for solving the gym cartpole env: https://github.com/deepmind/acme/blob/master/examples/control/run_d4pg_gym.py
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Spec for RL agent implementation?
Acme has a slightly different one: https://github.com/deepmind/acme which includes specs for agents, buffers etc. It is very general. You can see their component description here: https://github.com/deepmind/acme/blob/master/docs/components.md
What are some alternatives?
modelicagym - Modelica models integration with Open AI Gym
dm_env - A Python interface for reinforcement learning environments
Mava - 🦁 A research-friendly codebase for fast experimentation of multi-agent reinforcement learning in JAX
dm_control - Google DeepMind's software stack for physics-based simulation and Reinforcement Learning environments, using MuJoCo.
MPO - Pytorch implementation of "Maximum a Posteriori Policy Optimization" with Retrace for Discrete gym environments
tonic - Tonic RL library
selfhosted-apps-docker - Guide by Example
gym - A toolkit for developing and comparing reinforcement learning algorithms.
epymarl - An extension of the PyMARL codebase that includes additional algorithms and environment support
tmrl - Reinforcement Learning for real-time applications - host of the TrackMania Roborace League
rlpyt - Reinforcement Learning in PyTorch
pfrl - PFRL: a PyTorch-based deep reinforcement learning library