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- .gitattributes +1 -0
- .summary/0/events.out.tfevents.1703604106.rhmmedcatt-proliant-ml350-gen10 +3 -0
- .summary/1/events.out.tfevents.1703604106.rhmmedcatt-proliant-ml350-gen10 +3 -0
- README.md +189 -0
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- checkpoint_p1/best_000070736_18112512_reward_9359.436.pth +3 -0
.gitattributes
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.summary/0/events.out.tfevents.1703604106.rhmmedcatt-proliant-ml350-gen10
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README.md
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---
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library_name: sample-factory
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tags:
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- deep-reinforcement-learning
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- reinforcement-learning
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- sample-factory
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model-index:
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- name: APPO
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results:
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- task:
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type: reinforcement-learning
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name: reinforcement-learning
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dataset:
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name: mujoco_doublependulum
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type: mujoco_doublependulum
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metrics:
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- type: mean_reward
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value: 7575.65 +/- 3557.95
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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,
|
53 |
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"num_policies": 2,
|
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"async_rl": true,
|
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"serial_mode": false,
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"batched_sampling": true,
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"num_batches_to_accumulate": 2,
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+
"worker_num_splits": 1,
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"policy_workers_per_policy": 1,
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"max_policy_lag": 1000,
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"num_workers": 16,
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"num_envs_per_worker": 2,
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"batch_size": 1024,
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"num_batches_per_epoch": 8,
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"num_epochs": 4,
|
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"rollout": 128,
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"recurrence": 1,
|
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"shuffle_minibatches": false,
|
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"gamma": 0.99,
|
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"reward_scale": 1.0,
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"reward_clip": 1000.0,
|
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"value_bootstrap": false,
|
73 |
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"normalize_returns": true,
|
74 |
+
"exploration_loss_coeff": 0.0004677351413,
|
75 |
+
"value_loss_coeff": 0.5,
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76 |
+
"kl_loss_coeff": 0.0,
|
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+
"exploration_loss": "entropy",
|
78 |
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"gae_lambda": 0.95,
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"ppo_clip_ratio": 0.1,
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80 |
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"ppo_clip_value": 1.0,
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"with_vtrace": true,
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"vtrace_rho": 1.0,
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"vtrace_c": 1.0,
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"optimizer": "adam",
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"adam_eps": 1e-05,
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"adam_beta1": 0.9,
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"adam_beta2": 0.999,
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"max_grad_norm": 0.0,
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"learning_rate": 0.0003033891184,
|
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"lr_schedule": "linear_decay",
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"lr_schedule_kl_threshold": 0.008,
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"lr_adaptive_min": 1e-06,
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"lr_adaptive_max": 0.01,
|
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+
"obs_subtract_mean": 0.0,
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"obs_scale": 255.0,
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"normalize_input": true,
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"normalize_input_keys": [
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"obs"
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],
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"decorrelate_experience_max_seconds": 0,
|
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"decorrelate_envs_on_one_worker": true,
|
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+
"actor_worker_gpus": [],
|
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"set_workers_cpu_affinity": true,
|
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"force_envs_single_thread": false,
|
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"default_niceness": 0,
|
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"log_to_file": true,
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"experiment_summaries_interval": 3,
|
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"flush_summaries_interval": 30,
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"stats_avg": 100,
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"summaries_use_frameskip": true,
|
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+
"heartbeat_interval": 10,
|
112 |
+
"heartbeat_reporting_interval": 60,
|
113 |
+
"train_for_env_steps": 100000000,
|
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"train_for_seconds": 10000000000,
|
115 |
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"save_every_sec": 120,
|
116 |
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"keep_checkpoints": 2,
|
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"load_checkpoint_kind": "latest",
|
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"save_milestones_sec": 1200,
|
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"save_best_every_sec": 5,
|
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"save_best_metric": "reward",
|
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"save_best_after": 100000,
|
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"benchmark": false,
|
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"encoder_mlp_layers": [
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512,
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+
512
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],
|
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"encoder_conv_architecture": "convnet_atari",
|
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"encoder_conv_mlp_layers": [
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512
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],
|
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"use_rnn": false,
|
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"rnn_size": 512,
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"rnn_type": "gru",
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"rnn_num_layers": 1,
|
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+
"decoder_mlp_layers": [],
|
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+
"nonlinearity": "relu",
|
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+
"policy_initialization": "orthogonal",
|
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+
"policy_init_gain": 1.0,
|
139 |
+
"actor_critic_share_weights": true,
|
140 |
+
"adaptive_stddev": false,
|
141 |
+
"continuous_tanh_scale": 0.0,
|
142 |
+
"initial_stddev": 1.0,
|
143 |
+
"use_env_info_cache": false,
|
144 |
+
"env_gpu_actions": false,
|
145 |
+
"env_gpu_observations": true,
|
146 |
+
"env_frameskip": 4,
|
147 |
+
"env_framestack": 4,
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+
"pixel_format": "CHW"
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}
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```
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+
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A(n) **APPO** impala model trained on the **mujoco_doublependulum** 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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|
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## Downloading the model
|
163 |
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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-mujoco_doublependulum
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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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```
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python -m sf_examples.mujoco.enjoy_mujoco --algo=APPO --env=mujoco_doublependulum --train_dir=./train_dir --experiment=APPO-mujoco_doublependulum
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```
|
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|
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|
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You can also upload models to the Hugging Face Hub using the same script with the `--push_to_hub` flag.
|
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See https://www.samplefactory.dev/10-huggingface/huggingface/ for more details
|
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## Training with this model
|
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|
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To continue training with this model, use the `train` script corresponding to this environment:
|
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```
|
185 |
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python -m sf_examples.mujoco.train_mujoco --algo=APPO --env=mujoco_doublependulum --train_dir=./train_dir --experiment=APPO-mujoco_doublependulum --restart_behavior=resume --train_for_env_steps=10000000000
|
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```
|
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|
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Note, you may have to adjust `--train_for_env_steps` to a suitably high number as the experiment will resume at the number of steps it concluded at.
|
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|
checkpoint_p0/best_000329032_84246528_reward_9359.754.pth
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