dreamerv2
panda-gym
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| dreamerv2 | panda-gym | |
|---|---|---|
| 4 | 3 | |
| 853 | 437 | |
| - | - | |
| 0.0 | 5.3 | |
| about 1 year ago | 4 months ago | |
| Python | Python | |
| MIT License | MIT License |
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dreamerv2
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Sources of Actor Gradients
In fact, they found that just reinforce gradients work in DM control now too: Dreamerv2 GitHub (they just needed to turn off gradients through the action path - which I guess was being passed back with straight-through estimation? I'm actually having a difficult time telling how the gradient is different on the action vs policy.log_prob(action)).
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PyDreamer: model-based RL written in PyTorch + integrations with DM Lab and MineRL environments
This is my implementation of Hafner et al. DreamerV2 algorithm. I found the PlaNet/Dreamer/DreamerV2 paper series to be some of the coolest RL research in recent years, showing convincingly that MBRL (model-based RL) does work and is competitive with model-free algorithms. And we all know that AGI will be model-based, right? :)
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Any current state or the art libraries for training agents to play atari games?
Last I checked, for running off a single node, the state of the art was Dreamerv2 https://github.com/danijar/dreamerv2
- Google AI, DeepMind And The University of Toronto Introduce DreamerV2, The First Reinforcement Learning (RL) Agent That Outperforms Humans on The Atari Benchmark
panda-gym
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Hyperparameters for pick&place with Franka Emika manipulator
I'm trying to solve pick&place (and possibly also the other tasks in this repository) with Franka Emika Panda manipulator implemented in Mujoco. I've tried for long with stable_baseline3 but without any results, someone told me to try with RLLib because has better implementation (?), but still I can't find any solution...
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SAFE-PANDA-GYM a modification to Panda - Gym to train Safe-RL agents
We develop a modification to the Panda Gym by adding constraints to the environments like Unsafe regions and, constraints on the task. The aim is to develop an environment to test CMDPs (Constraint Markov Decision Process) / Safe-RL algorithms such as CPO, PPO - Lagrangian and algorithms developed by the team. Agents would not only have to come up with optimal policy for control and planning but also ensure they don't violate a constraint.
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Did anyone try Panda-Gym?
The acquisition of Mujoco makes Openai to remove the robotics from their repo. I had no choice but to find an alternative. Then I found https://github.com/qgallouedec/panda-gym which is built on PyBullet.
What are some alternatives?
dreamerv3 - Mastering Diverse Domains through World Models
dm_env - A Python interface for reinforcement learning environments
dreamer - Dream to Control: Learning Behaviors by Latent Imagination
stable-baselines3 - PyTorch version of Stable Baselines, reliable implementations of reinforcement learning algorithms.
dm_control - Google DeepMind's software stack for physics-based simulation and Reinforcement Learning environments, using MuJoCo.
sapai - Super auto pets engine built with reinforment learning training in mind
stable-baselines3-contrib - Contrib package for Stable-Baselines3 - Experimental reinforcement learning (RL) code
habitat-api - A modular high-level library to train embodied AI agents across a variety of tasks, environments, and simulators. [Moved to: https://github.com/facebookresearch/habitat-lab]
planet - Learning Latent Dynamics for Planning from Pixels
orion - Asynchronous Distributed Hyperparameter Optimization.
Safe-panda-gym - OpenaAI Gym Franka Emika Panda robot environment based on PyBullet.