--- library_name: sample-factory tags: - deep-reinforcement-learning - reinforcement-learning - sample-factory model-index: - name: APPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: mujoco_pendulum type: mujoco_pendulum metrics: - type: mean_reward value: 1000.00 +/- 0.00 name: mean_reward verified: false --- ## About the Project 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). 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. ## Project Aims 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. 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. 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. 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') ## About the Model 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: ``` hyperparameters = { "help": false, "algo": "APPO", "env": "atari_asteroid", "experiment": "atari_asteroid_APPO", "train_dir": "./train_atari", "restart_behavior": "restart", "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": true, "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, "pixel_format": "CHW" } ``` A(n) **APPO** impala model trained on the **mujoco_pendulum** environment. This model was trained using Sample-Factory 2.0: https://github.com/alex-petrenko/sample-factory. Sample factory is a high throughput on-policy RL framework. I have been using Documentation for how to use Sample-Factory can be found at https://www.samplefactory.dev/ ## Downloading the model After installing Sample-Factory, download the model with: ``` python -m sample_factory.huggingface.load_from_hub -r MattStammers/APPO-mujoco_pendulum ``` ## Using the model To run the model after download, use the `enjoy` script corresponding to this environment: ``` python -m sf_examples.mujoco.enjoy_mujoco --algo=APPO --env=mujoco_pendulum --train_dir=./train_dir --experiment=APPO-mujoco_pendulum ``` 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.mujoco.train_mujoco --algo=APPO --env=mujoco_pendulum --train_dir=./train_dir --experiment=APPO-mujoco_pendulum --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.