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appo-atari_spaceinvaders-superhuman

Reinforcement Learning
sample-factory
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deep-reinforcement-learning
Eval Results (legacy)
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README.md CHANGED
@@ -15,35 +15,39 @@ model-index:
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  type: atari_spaceinvaders
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  metrics:
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  - type: mean_reward
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- value: 2642.00 +/- 352.66
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  name: mean_reward
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  verified: false
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  ---
22
 
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- A(n) **APPO** model trained on the **atari_spaceinvaders** environment.
24
 
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- This model was trained using Sample-Factory 2.0: https://github.com/alex-petrenko/sample-factory.
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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_spaceinvaders
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- ```
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-
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- ## About the Model
 
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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.
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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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- The hyperparameters used in the model are the ones I have pushed to my fork of sample-factory: https://github.com/MattStammers/sample-factory. Given that https://huggingface.co/edbeeching has kindly shared his.
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- I saved time and energy by using many of his tuned hyperparameters to maximise performance. However, he used 2 billion training steps. I have started as explained above at 10 million then moved to 100m to see how performance goes:
 
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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,
@@ -141,12 +145,28 @@ hyperparameters = {
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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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  ```
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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_spaceinvaders
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  metrics:
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  - type: mean_reward
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+ value: 40227.50 +/- 20536.35
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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.
 
 
 
32
 
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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.
34
+
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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.
36
 
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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')
38
 
 
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+ ## About the Model
41
+
42
+ 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:
43
  ```
44
  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_spaceinvaders** environment.
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+
157
+ This model was trained using Sample-Factory 2.0: https://github.com/alex-petrenko/sample-factory. Sample factory is a
158
+ high throughput on-policy RL framework. I have been using
159
+ Documentation for how to use Sample-Factory can be found at https://www.samplefactory.dev/
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+
161
+
162
+ ## Downloading the model
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+
164
+ After installing Sample-Factory, download the model with:
165
+ ```
166
+ python -m sample_factory.huggingface.load_from_hub -r MattStammers/APPO-atari_spaceinvaders
167
+ ```
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+
169
+
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  ## Using the model
171
 
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  To run the model after download, use the `enjoy` script corresponding to this environment:
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