ConnectionResetError: [Errno 104] Connection reset by peer

[JinQiangWang2021]

AGENT NAME: A3C 1.1: A3C TITLE CartPole layer info [20, 10, [2, 1]] layer info [20, 10, [2, 1]] {'learning_rate': 0.005, 'linear_hidden_units': [20, 10], 'final_layer_activation': ['SOFTMAX', None], 'gradient_clipping_norm': 5.0, 'discount_rate': 0.99, 'epsilon_decay_rate_denominator': 1.0, 'normalise_rewards': True, 'exploration_worker_difference': 2.0, 'clip_rewards': False, 'Actor': {'learning_rate': 0.0003, 'linear_hidden_units': [64, 64], 'final_layer_activation': 'Softmax', 'batch_norm': False, 'tau': 0.005, 'gradient_clipping_norm': 5, 'initialiser': 'Xavier'}, 'Critic': {'learning_rate': 0.0003, 'linear_hidden_units': [64, 64], 'final_layer_activation': None, 'batch_norm': False, 'buffer_size': 1000000, 'tau': 0.005, 'gradient_clipping_norm': 5, 'initialiser': 'Xavier'}, 'min_steps_before_learning': 400, 'batch_size': 256, 'mu': 0.0, 'theta': 0.15, 'sigma': 0.25, 'action_noise_std': 0.2, 'action_noise_clipping_range': 0.5, 'update_every_n_steps': 1, 'learning_updates_per_learning_session': 1, 'automatically_tune_entropy_hyperparameter': True, 'entropy_term_weight': None, 'add_extra_noise': False, 'do_evaluation_iterations': True, 'output_activation': None, 'hidden_activations': 'relu', 'dropout': 0.0, 'initialiser': 'default', 'batch_norm': False, 'columns_of_data_to_be_embedded': [], 'embedding_dimensions': [], 'y_range': ()} RANDOM SEED 1044929444 Episode 4892, Score: 10.00, Max score seen: 88.00, Rolling score: 9.33, Max rolling score seen: 25.27Process Process-1: Traceback (most recent call last): File "/home/account/anaconda3/envs/RL17/lib/python3.7/multiprocessing/process.py", line 297, in _bootstrap self.run() File "/home/account/anaconda3/envs/RL17/lib/python3.7/multiprocessing/process.py", line 99, in run self._target(*self._args, **self._kwargs) File "/home/account/Documents/Deep_RL_Implementations/agents/actor_critic_agents/A3C.py", line 70, in update_shared_model gradients = gradient_updates_queue.get() File "/home/account/anaconda3/envs/RL17/lib/python3.7/multiprocessing/queues.py", line 113, in get return _ForkingPickler.loads(res) File "/home/account/anaconda3/envs/RL17/lib/python3.7/site-packages/torch/multiprocessing/reductions.py", line 151, in rebuild_storage_fd fd = df.detach() File "/home/account/anaconda3/envs/RL17/lib/python3.7/multiprocessing/resource_sharer.py", line 57, in detach with _resource_sharer.get_connection(self._id) as conn: File "/home/account/anaconda3/envs/RL17/lib/python3.7/multiprocessing/resource_sharer.py", line 87, in get_connection c = Client(address, authkey=process.current_process().authkey) File "/home/account/anaconda3/envs/RL17/lib/python3.7/multiprocessing/connection.py", line 492, in Client c = SocketClient(address) File "/home/account/anaconda3/envs/RL17/lib/python3.7/multiprocessing/connection.py", line 620, in SocketClient s.connect(address) FileNotFoundError: [Errno 2] No such file or directory

I try to use this medod : https://github.com/p-christ/Deep-Reinforcement-Learning-Algorithms-with-PyTorch/issues/45 change python Quenue instead of torch.multiprocessing Queue**

but new question is ：

1.1: A3C TITLE CartPole layer info [20, 10, [2, 1]] layer info [20, 10, [2, 1]] {'learning_rate': 0.005, 'linear_hidden_units': [20, 10], 'final_layer_activation': ['SOFTMAX', None], 'gradient_clipping_norm': 5.0, 'discount_rate': 0.99, 'epsilon_decay_rate_denominator': 1.0, 'normalise_rewards': True, 'exploration_worker_difference': 2.0, 'clip_rewards': False, 'Actor': {'learning_rate': 0.0003, 'linear_hidden_units': [64, 64], 'final_layer_activation': 'Softmax', 'batch_norm': False, 'tau': 0.005, 'gradient_clipping_norm': 5, 'initialiser': 'Xavier'}, 'Critic': {'learning_rate': 0.0003, 'linear_hidden_units': [64, 64], 'final_layer_activation': None, 'batch_norm': False, 'buffer_size': 1000000, 'tau': 0.005, 'gradient_clipping_norm': 5, 'initialiser': 'Xavier'}, 'min_steps_before_learning': 400, 'batch_size': 256, 'mu': 0.0, 'theta': 0.15, 'sigma': 0.25, 'action_noise_std': 0.2, 'action_noise_clipping_range': 0.5, 'update_every_n_steps': 1, 'learning_updates_per_learning_session': 1, 'automatically_tune_entropy_hyperparameter': True, 'entropy_term_weight': None, 'add_extra_noise': False, 'do_evaluation_iterations': True, 'output_activation': None, 'hidden_activations': 'relu', 'dropout': 0.0, 'initialiser': 'default', 'batch_norm': False, 'columns_of_data_to_be_embedded': [], 'embedding_dimensions': [], 'y_range': ()} RANDOM SEED 2824610793 Episode 999, Score: 11.00, Max score seen: 99.00, Rolling score: 11.18, Max rolling score seen: 31.03Traceback (most recent call last): File "/home/account/anaconda3/envs/RL17/lib/python3.7/multiprocessing/resource_sharer.py", line 142, in _serve with self._listener.accept() as conn: File "/home/account/anaconda3/envs/RL17/lib/python3.7/multiprocessing/connection.py", line 455, in accept deliver_challenge(c, self._authkey) File "/home/account/anaconda3/envs/RL17/lib/python3.7/multiprocessing/connection.py", line 730, in deliver_challenge response = connection.recv_bytes(256) # reject large message File "/home/account/anaconda3/envs/RL17/lib/python3.7/multiprocessing/connection.py", line 216, in recv_bytes buf = self._recv_bytes(maxlength) File "/home/account/anaconda3/envs/RL17/lib/python3.7/multiprocessing/connection.py", line 407, in _recv_bytes buf = self._recv(4) File "/home/account/anaconda3/envs/RL17/lib/python3.7/multiprocessing/connection.py", line 379, in _recv chunk = read(handle, remaining) ConnectionResetError: [Errno 104] Connection reset by peer Time taken: 7.811599016189575

[Albert723]

Hello, I have the same problem as you when implementing the A3C algorithm, have you solved it?

[fry404006308]

您好，我是范仁义，您的邮件我已经收到，我会尽快处理，谢谢。