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.

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.

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. 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:

hyperparameters =  {
  "device": "gpu",
  "seed": 1234,
  "num_policies": 2,
  "async_rl": true,
  "serial_mode": false,
  "batched_sampling": true,
  "num_batches_to_accumulate": 2,
  "worker_num_splits": 1,
  "policy_workers_per_policy": 1,
  "max_policy_lag": 1000,
  "num_workers": 16,
  "num_envs_per_worker": 2,
  "batch_size": 1024,
  "num_batches_per_epoch": 8,
  "num_epochs": 4,
  "rollout": 128,
  "recurrence": 1,
  "shuffle_minibatches": false,
  "gamma": 0.99,
  "reward_scale": 1.0,
  "reward_clip": 1000.0,
  "value_bootstrap": false,
  "normalize_returns": true,
  "exploration_loss_coeff": 0.0004677351413,
  "value_loss_coeff": 0.5,
  "kl_loss_coeff": 0.0,
  "exploration_loss": "entropy",
  "gae_lambda": 0.95,
  "ppo_clip_ratio": 0.1,
  "ppo_clip_value": 1.0,
  "with_vtrace": false,
  "vtrace_rho": 1.0,
  "vtrace_c": 1.0,
  "optimizer": "adam",
  "adam_eps": 1e-05,
  "adam_beta1": 0.9,
  "adam_beta2": 0.999,
  "max_grad_norm": 0.0,
  "learning_rate": 0.0003033891184,
  "lr_schedule": "linear_decay",
  "lr_schedule_kl_threshold": 0.008,
  "lr_adaptive_min": 1e-06,
  "lr_adaptive_max": 0.01,
  "obs_subtract_mean": 0.0,
  "obs_scale": 255.0,
  "normalize_input": true,
  "normalize_input_keys": [
    "obs"
  ],
  "decorrelate_experience_max_seconds": 0,
  "decorrelate_envs_on_one_worker": true,
  "actor_worker_gpus": [],
  "set_workers_cpu_affinity": true,
  "force_envs_single_thread": false,
  "default_niceness": 0,
  "log_to_file": true,
  "experiment_summaries_interval": 3,
  "flush_summaries_interval": 30,
  "stats_avg": 100,
  "summaries_use_frameskip": true,
  "heartbeat_interval": 10,
  "heartbeat_reporting_interval": 60,
  "train_for_env_steps": 100000000,
  "train_for_seconds": 10000000000,
  "save_every_sec": 120,
  "keep_checkpoints": 2,
  "load_checkpoint_kind": "latest",
  "save_milestones_sec": 1200,
  "save_best_every_sec": 5,
  "save_best_metric": "reward",
  "save_best_after": 100000,
  "benchmark": false,
  "encoder_mlp_layers": [
    512,
    512
  ],
  "encoder_conv_architecture": "convnet_atari",
  "encoder_conv_mlp_layers": [
    512
  ],
  "use_rnn": false,
  "rnn_size": 512,
  "rnn_type": "gru",
  "rnn_num_layers": 1,
  "decoder_mlp_layers": [],
  "nonlinearity": "relu",
  "policy_initialization": "orthogonal",
  "policy_init_gain": 1.0,
  "actor_critic_share_weights": true,
  "adaptive_stddev": false,
  "continuous_tanh_scale": 0.0,
  "initial_stddev": 1.0,
  "use_env_info_cache": false,
  "env_gpu_actions": false,
  "env_gpu_observations": true,
  "env_frameskip": 4,
  "env_framestack": 4,
  }

Using the model

To run the model after download, use the enjoy script corresponding to this environment:

python -m sf_examples.atari.enjoy_atari --algo=APPO --env=atari_pong --train_dir=./train_dir --experiment=APPO-atari_pong

You can also upload models to the Hugging Face Hub using the same script with the --push_to_hub flag. See https://www.samplefactory.dev/10-huggingface/huggingface/ for more details

Training with this model

To continue training with this model, use the train script corresponding to this environment:

python -m sf_examples.atari.train_atari --algo=APPO --env=atari_pong --train_dir=./train_dir --experiment=APPO-atari_pong --restart_behavior=resume --train_for_env_steps=10000000000

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.

MattStammers/appo-atari_pong-sota

Downloading the model

About the Model

Using the model

Training with this model

MattStammers/
appo-atari_pong-sota