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36 lines
1.5 KiB
Markdown
36 lines
1.5 KiB
Markdown
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# Decentralized Distributed Proximal Policy Optimization (DDPPO)
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## Overview
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[PPO](https://arxiv.org/abs/1707.06347) is a model-free on-policy RL algorithm that works
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well for both discrete and continuous action space environments. PPO utilizes an
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actor-critic framework, where there are two networks, an actor (policy network) and
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critic network (value function).
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## Distributed PPO Algorithms
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### Distributed baseline PPO
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[See implementation here](https://github.com/ray-project/ray/blob/master/rllib/algorithms/ppo/ppo.py)
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### Asychronous PPO (APPO)
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[See implementation here](https://github.com/ray-project/ray/blob/master/rllib/algorithms/appo/appo.py)
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### Decentralized Distributed PPO (DDPPO) ..
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.. removes the assumption that gradient-updates must
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be done on a central node. Instead, gradients are computed remotely on each data
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collection node and all-reduced at each mini-batch using torch distributed. This allows
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each worker’s GPU to be used both for sampling and for training.
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[See implementation here](https://github.com/ray-project/ray/blob/master/rllib/algorithms/ddppo/ddppo.py)
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## Documentation & Implementation:
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### [Decentralized Distributed Proximal Policy Optimization (DDPPO)](https://arxiv.org/abs/1911.00357)
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**[Detailed Documentation](https://docs.ray.io/en/master/rllib-algorithms.html#decentralized-distributed-proximal-policy-optimization-dd-ppo)**
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**[Implementation](https://github.com/ray-project/ray/blob/master/rllib/algorithms/ddppo/ddppo.py)**
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