Hello!!! I need your help! I have created my own environment and just want one agent to walk from one point to another. I have tried to implement the existing examples in Unity - ML-Agents, so to understand what exactly have to be implemented, with succeess following the instructions of the tutorial. But in my own environment, I do something wrong. If someone could help me , I would be very gratefull!!! Thank you in advance!!! When I try to train it, it appers these hugeeeeeee error message: Traceback (most recent call last): File "c:\python36\lib\site-packages\tensorflow\python\client\session.py", line 1327, in _do_call return fn(*args) File "c:\python36\lib\site-packages\tensorflow\python\client\session.py", line 1312, in _run_fn options, feed_dict, fetch_list, target_list, run_metadata) File "c:\python36\lib\site-packages\tensorflow\python\client\session.py", line 1420, in _call_tf_sessionrun status, run_metadata) File "c:\python36\lib\site-packages\tensorflow\python\framework\errors_impl.py", line 516, in __exit__ c_api.TF_GetCode(self.status.status)) tensorflow.python.framework.errors_impl.InvalidArgumentError: Reshape cannot infer the missing input size for an empty tensor unless all specified input sizes are non-zero [[Node: softmax_cross_entropy_with_logits/Reshape_1 = Reshape[T=DT_FLOAT, Tshape=DT_INT32, _device="/job:localhost/replica:0/task:0/device:CPU:0"](Softmax_4, softmax_cross_entropy_with_logits/concat_1)]] During handling of the above exception, another exception occurred: Traceback (most recent call last): File "C:\Python36\Scripts\mlagents-learn-script.py", line 11, in <module> load_entry_point('mlagents', 'console_scripts', 'mlagents-learn')() File "c:\ml-agents\ml-agents\mlagents\trainers\learn.py", line 417, in main run_training(0, run_seed, options, Queue()) File "c:\ml-agents\ml-agents\mlagents\trainers\learn.py", line 255, in run_training tc.start_learning(env) File "c:\ml-agents\ml-agents\mlagents\trainers\trainer_controller.py", line 202, in start_learning n_steps = self.advance(env_manager) File "c:\ml-agents\ml-agents-envs\mlagents\envs\timers.py", line 263, in wrapped return func(*args, **kwargs) File "c:\ml-agents\ml-agents\mlagents\trainers\trainer_controller.py", line 269, in advance new_step_infos = env.step() File "c:\ml-agents\ml-agents-envs\mlagents\envs\subprocess_env_manager.py", line 175, in step self._queue_steps() run_metadata) File "c:\python36\lib\site-packages\tensorflow\python\client\session.py", line 1340, in _do_call raise type(e)(node_def, op, message) tensorflow.python.framework.errors_impl.InvalidArgumentError: Reshape cannot infer the missing input size for an empty tensor unless all specified input sizes are non-zero [[Node: softmax_cross_entropy_with_logits/Reshape_1 = Reshape[T=DT_FLOAT, Tshape=DT_INT32, _device="/job:localhost/replica:0/task:0/device:CPU:0"](Softmax_4, softmax_cross_entropy_with_logits/concat_1)]] Caused by op 'softmax_cross_entropy_with_logits/Reshape_1', defined at: File "C:\Python36\Scripts\mlagents-learn-script.py", line 11, in <module> load_entry_point('mlagents', 'console_scripts', 'mlagents-learn')() File "c:\ml-agents\ml-agents\mlagents\trainers\learn.py", line 417, in main run_training(0, run_seed, options, Queue()) File "c:\ml-agents\ml-agents\mlagents\trainers\learn.py", line 233, in run_training options.multi_gpu, File "c:\ml-agents\ml-agents\mlagents\trainers\trainer_util.py", line 91, in initialize_trainers multi_gpu, File "c:\ml-agents\ml-agents\mlagents\trainers\ppo\trainer.py", line 75, in __init__ seed, brain, trainer_parameters, self.is_training, load File "c:\ml-agents\ml-agents\mlagents\trainers\ppo\policy.py", line 40, in __init__ brain, trainer_params, reward_signal_configs, is_training, load, seed File "c:\ml-agents\ml-agents\mlagents\trainers\ppo\policy.py", line 91, in create_model trainer_params.get("vis_encode_type", "simple") File "c:\ml-agents\ml-agents\mlagents\trainers\ppo\models.py", line 55, in __init__ self.create_dc_actor_critic(h_size, num_layers, vis_encode_type) File "c:\ml-agents\ml-agents\mlagents\trainers\ppo\models.py", line 255, in create_dc_actor_critic File "c:\ml-agents\ml-agents-envs\mlagents\envs\subprocess_env_manager.py", line 168, in _queue_steps env_action_info = self._take_step(env_worker.previous_step) File "c:\ml-agents\ml-agents-envs\mlagents\envs\timers.py", line 263, in wrapped return func(*args, **kwargs) File "c:\ml-agents\ml-agents-envs\mlagents\envs\subprocess_env_manager.py", line 268, in _take_step brain_info File "c:\ml-agents\ml-agents\mlagents\trainers\tf_policy.py", line 126, in get_action run_out = self.evaluate(brain_info) File "c:\ml-agents\ml-agents-envs\mlagents\envs\timers.py", line 263, in wrapped return func(*args, **kwargs) File "c:\ml-agents\ml-agents\mlagents\trainers\ppo\policy.py", line 162, in evaluate run_out = self._execute_model(feed_dict, self.inference_dict) File "c:\ml-agents\ml-agents\mlagents\trainers\tf_policy.py", line 151, in _execute_model network_out = self.sess.run(list(out_dict.values()), feed_dict=feed_dict) File "c:\python36\lib\site-packages\tensorflow\python\client\session.py", line 905, in run run_metadata_ptr) File "c:\python36\lib\site-packages\tensorflow\python\client\session.py", line 1140, in _run feed_dict_tensor, options, run_metadata) File "c:\python36\lib\site-packages\tensorflow\python\client\session.py", line 1321, in _do_run File "c:\ml-agents\ml-agents\mlagents\trainers\ppo\models.py", line 255, in create_dc_actor_critic for i in range(len(self.act_size)) File "c:\ml-agents\ml-agents\mlagents\trainers\ppo\models.py", line 255, in <listcomp> for i in range(len(self.act_size)) File "c:\python36\lib\site-packages\tensorflow\python\ops\nn_ops.py", line 1869, in softmax_cross_entropy_with_logits_v2 labels = _flatten_outer_dims(labels) File "c:\python36\lib\site-packages\tensorflow\python\ops\nn_ops.py", line 1616, in _flatten_outer_dims output = array_ops.reshape(logits, array_ops.concat([[-1], last_dim_size], 0)) File "c:\python36\lib\site-packages\tensorflow\python\ops\gen_array_ops.py", line 6980, in reshape "Reshape", tensor=tensor, shape=shape, name=name) File "c:\python36\lib\site-packages\tensorflow\python\framework\op_def_library.py", line 787, in _apply_op_helper op_def=op_def) File "c:\python36\lib\site-packages\tensorflow\python\framework\ops.py", line 3290, in create_op op_def=op_def) File "c:\python36\lib\site-packages\tensorflow\python\framework\ops.py", line 1654, in __init__ self._traceback = self._graph._extract_stack() # pylint: disable=protected-access InvalidArgumentError (see above for traceback): Reshape cannot infer the missing input size for an empty tensor unless all specified input sizes are non-zero [[Node: softmax_cross_entropy_with_logits/Reshape_1 = Reshape[T=DT_FLOAT, Tshape=DT_INT32, _device="/job:localhost/replica:0/task:0/device:CPU:0"](Softmax_4, softmax_cross_entropy_with_logits/concat_1)]]
Hi, You seem to be using an older version of ml-agents. Can you try again on the latest release and tell us if you still see this error ?
Yes I am using mlagents 0.10. Fortunately I have found the solution !!! Using this version I had not define correct the Brain parameters in the inspector window.(Vector action -> Branch descriptions). Thank you for your time!!!