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- .gitattributes +1 -0
- .summary/0/events.out.tfevents.1700946348.rhmmedcatt-proliant-ml350-gen10 +3 -0
- .summary/1/events.out.tfevents.1700946348.rhmmedcatt-proliant-ml350-gen10 +3 -0
- README.md +36 -16
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.gitattributes
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.summary/0/events.out.tfevents.1700946348.rhmmedcatt-proliant-ml350-gen10
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README.md
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type: atari_seaquest
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metrics:
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- type: mean_reward
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value:
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name: mean_reward
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verified: false
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---
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This
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Documentation for how to use Sample-Factory can be found at https://www.samplefactory.dev/
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##
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```
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python -m sample_factory.huggingface.load_from_hub -r MattStammers/APPO-atari_seaquest
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```
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The aim is to reach state-of-the-art (SOTA) performance on each atari environment. I will flag the models with SOTA when they reach at or near these levels.
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```
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hyperparameters = {
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"device": "gpu",
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"seed": 1234,
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"num_policies": 2,
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"env_gpu_observations": true,
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"env_frameskip": 4,
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"env_framestack": 4,
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```
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## Using the model
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To run the model after download, use the `enjoy` script corresponding to this environment:
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type: atari_seaquest
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metrics:
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- type: mean_reward
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value: 2874.00 +/- 12.81
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name: mean_reward
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verified: false
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---
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## About the Project
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This project is an attempt to maximise performance of high sample throughput APPO RL models in Atari environments in as carbon efficient a manner as possible using a single, not particularly high performance single machine. It is about demonstrating the generalisability of on-policy algorithms to create good performance quickly (by sacrificing sample efficiency) while also proving that this route to RL production is accessible to even hobbyists like me (I am a gastroenterologist not a computer scientist).
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In terms of throughput I am managing to reach throughputs of 2,500 - 3,000 across both policies using sample factory using two Quadro P2200's (not particularly powerful GPUs) each loaded up about 60% (3GB). Previously using the stable baselines 3 (sb3) implementation of PPO it would take about a week to train an atari agent to 100 million timesteps synchronously. By comparison the sample factory async implementation takes only just over 2 hours to achieve the same result. That is about 84 times faster with only typically a 21 watt burn per GPU. I am thus very grateful to Alex Petrenko and all the sample factory team for their work on this.
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## Project Aims
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This model as with all the others in the benchmarks was trained initially asynchronously un-seeded to 10 million steps for the purposes of setting a sample factory async baseline for this model on this environment but only 3/57 made it anywhere near sota performance.
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I then re-trained the models with 100 million timesteps- at this point 2 environments maxed out at sota performance (Pong and Freeway) with four approaching sota performance - (atlantis, boxing, tennis and fishingderby.) =6/57 near sota.
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The aim now is to try and reach state-of-the-art (SOTA) performance on a further block of atari environments using up to 1 billion training timesteps initially with appo. I will flag the models with SOTA when they reach at or near these levels.
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After this I will switch on V-Trace to see if the Impala variations perform any better with the same seed (I have seeded '1234')
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## About the Model
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The hyperparameters used in the model are described in my shell script on my fork of sample-factory: https://github.com/MattStammers/sample-factory. Given that https://huggingface.co/edbeeching has kindly shared his parameters, I saved time and energy by using many of his tuned hyperparameters to reduce carbon inefficiency:
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```
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hyperparameters = {
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"help": false,
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"algo": "APPO",
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"env": "atari_asteroid",
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"experiment": "atari_asteroid_APPO",
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"train_dir": "./train_atari",
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"restart_behavior": "restart",
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"device": "gpu",
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"seed": 1234,
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"num_policies": 2,
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"env_gpu_observations": true,
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"env_frameskip": 4,
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"env_framestack": 4,
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"pixel_format": "CHW"
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}
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```
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A(n) **APPO** model trained on the **atari_seaquest** environment.
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This model was trained using Sample-Factory 2.0: https://github.com/alex-petrenko/sample-factory. Sample factory is a
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high throughput on-policy RL framework. I have been using
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Documentation for how to use Sample-Factory can be found at https://www.samplefactory.dev/
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## Downloading the model
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After installing Sample-Factory, download the model with:
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```
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python -m sample_factory.huggingface.load_from_hub -r MattStammers/APPO-atari_seaquest
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```
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## Using the model
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To run the model after download, use the `enjoy` script corresponding to this environment:
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