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Incorporate distributed RL framework, Ape-X and Ape-X DQN (#246)
* Take context as init_communication input; all processes share the same context. * implement abstract classes for distributed and ApeX Learner wrapper * Implement params2numpy method that loads torch state_dict as array of np.ndarray. * add __init__ * implement worker as abstract class, not wrapper base class * Change apex_learner file name to learner. * Implement Ape-X worker and learner base classes * implement Ape-X DQN worker * Create base class for distributed architectures * Implement and test Ape-X DQN working on Pong * Accept current change (master) for PongNoFrameskip-v4 dqn config * Make env_info more explicit in run_pong script (accept incoming change) * Make learner return cpu state_dict (accept incoming change) * Fix minor errors * Implement ApeXWorker as a wrapper ApeXWorkerWrapper Implement Logger and test wandb functionality Add worker and logger render in argparse Implement load_param() method in logger and worker * Move num_workers to hyperparams, and add logger_interval to hyperparams. * Implement safe exit condition for all ray actors. * Change _init_communication -> init_communication and call outside of __init__ for all ApeX actors Implement test() in distributed architectures (load from checkpoint and run logger test()) * * Add documentation * Move collect_data from worker class to ApeX Wrapper * Change hyperparameters around * Add worker-verbose as argparse flag * * Move num_worker to hyper_param cfg * * Add author * Add separate integration test for ApeX * Add integration test flag to pong * argparse integration test flag store_false->store_true * Change default config to dqn. * * Log worker scores per update step on Wandb. * Modify integration test * Modify apex buffer config for integration test * Change distributed directory structure * Add documentation * Modify readme.md * Modify readme.md * Add Ape-X to README. * Add description about args flags for distributed training. Co-authored-by: khkim <kh.kim@medipixel.io> Co-authored-by: Kyunghwan Kim <khsyee@gmail.com>
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| Original file line number | Diff line number | Diff line change |
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| @@ -0,0 +1,77 @@ | ||
| """Config for ApeX-DQN on Pong-No_FrameSkip-v4. | ||
| - Author: Chris Yoon | ||
| - Contact: chris.yoon@medipixel.io | ||
| """ | ||
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| from rl_algorithms.common.helper_functions import identity | ||
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| agent = dict( | ||
| type="ApeX", | ||
| hyper_params=dict( | ||
| gamma=0.99, | ||
| tau=5e-3, | ||
| buffer_size=int(2.5e5), # openai baselines: int(1e4) | ||
| batch_size=512, # openai baselines: 32 | ||
| update_starts_from=int(1e5), # openai baselines: int(1e4) | ||
| multiple_update=1, # multiple learning updates | ||
| train_freq=1, # in openai baselines, train_freq = 4 | ||
| gradient_clip=10.0, # dueling: 10.0 | ||
| n_step=5, | ||
| w_n_step=1.0, | ||
| w_q_reg=0.0, | ||
| per_alpha=0.6, # openai baselines: 0.6 | ||
| per_beta=0.4, | ||
| per_eps=1e-6, | ||
| loss_type=dict(type="DQNLoss"), | ||
| # Epsilon Greedy | ||
| max_epsilon=1.0, | ||
| min_epsilon=0.1, # openai baselines: 0.01 | ||
| epsilon_decay=1e-6, # openai baselines: 1e-7 / 1e-1 | ||
| # grad_cam | ||
| grad_cam_layer_list=[ | ||
| "backbone.cnn.cnn_0.cnn", | ||
| "backbone.cnn.cnn_1.cnn", | ||
| "backbone.cnn.cnn_2.cnn", | ||
| ], | ||
| num_workers=4, | ||
| local_buffer_max_size=1000, | ||
| worker_update_interval=50, | ||
| logger_interval=2000, | ||
| ), | ||
| learner_cfg=dict( | ||
| type="DQNLearner", | ||
| device="cuda", | ||
| backbone=dict( | ||
| type="CNN", | ||
| configs=dict( | ||
| input_sizes=[4, 32, 64], | ||
| output_sizes=[32, 64, 64], | ||
| kernel_sizes=[8, 4, 3], | ||
| strides=[4, 2, 1], | ||
| paddings=[1, 0, 0], | ||
| ), | ||
| ), | ||
| head=dict( | ||
| type="DuelingMLP", | ||
| configs=dict( | ||
| use_noisy_net=False, hidden_sizes=[512], output_activation=identity | ||
| ), | ||
| ), | ||
| optim_cfg=dict( | ||
| lr_dqn=0.0003, # dueling: 6.25e-5, openai baselines: 1e-4 | ||
| weight_decay=0.0, # this makes saturation in cnn weights | ||
| adam_eps=1e-8, # rainbow: 1.5e-4, openai baselines: 1e-8 | ||
| ), | ||
| ), | ||
| worker_cfg=dict(type="DQNWorker", device="cpu",), | ||
| logger_cfg=dict(type="DQNLogger",), | ||
| comm_cfg=dict( | ||
| learner_buffer_port=6554, | ||
| learner_worker_port=6555, | ||
| worker_buffer_port=6556, | ||
| learner_logger_port=6557, | ||
| send_batch_port=6558, | ||
| priorities_port=6559, | ||
| ), | ||
| ) |
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,23 @@ | ||
| """Abstract class for distributed architectures. | ||
| - Author: Chris Yoon | ||
| - Contact: chris.yoon@medipixel.io | ||
| """ | ||
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| from abc import ABC, abstractmethod | ||
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| class Architecture(ABC): | ||
| """Abstract class for distributed architectures""" | ||
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| @abstractmethod | ||
| def _spawn(self): | ||
| pass | ||
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| @abstractmethod | ||
| def train(self): | ||
| pass | ||
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| @abstractmethod | ||
| def test(self): | ||
| pass |
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