hanruihua / rl_rvo_nav Goto Github PK

I can only obtain results in 4 robot settings, but when using weights to train 10 robots, I cannot obtain good results.

Hello author, I did not find the code quoted in this sentencefrom ir_sim.env import env_base in your project, does it have some effect ?

Hello, I only see in your paper and code about the reward for RVO collision avoidance, and there is no reward for finding the robot to find the target, how is it set about the robot finding the target?

运行代码时报错：AttributeError: 'ArtistList' object has no attribute 'clear' 。 这个错误是由于matplotlib的版本太高吗？？我的版本是matplotlib==3.5.1。

Hello author, may I ask that TypeError occurs when I run train_process for the first time: make() got an unexpected keyword argument 'world_name', of course, the following parameters are also redundant, I am a novice, hope the author can help point out

How do I get the final reward change chart?

I've changed the environment using examples in the usage of intelligent-robot-simulator, but where can I implement this environment in gym_env and policy_train_process? Thank you.

Basically, how should we add all the varied environments like dynamic obstacles and different shapes of obstacles from 'Intelligent robot simulator' to this project? i.e. which file do we need to add or edit, or the reward function...

I have a question about stage_1, why can't the result reach 100%?

policy_name: r4_0_50  successful rate: 12.00% average EpLen: 90.58 std length 9.24 average speed: 1.08 std speed 0.07
policy_name: r4_0_100  successful rate: 51.00% average EpLen: 91.39 std length 12.24 average speed: 0.93 std speed 0.09
policy_name: r4_0_150  successful rate: 39.00% average EpLen: 77.46 std length 7.88 average speed: 1.08 std speed 0.09
policy_name: r4_0_200  successful rate: 84.00% average EpLen: 64.77 std length 5.52 average speed: 1.27 std speed 0.1
policy_name: r4_0_250  successful rate: 72.00% average EpLen: 64.61 std length 5.3 average speed: 1.23 std speed 0.12

Where is the ir_sim file in from ir_sim.env import env_base?

Hello, I'm running train_process_s1.py,the model is saved in mode_ save, and then I run policy _ test.py,after I run the file an error occurred that the file could not be found.I want to ask the author what content this binary file holds（parser.add_argument('--arg_name', default='r4_17/r4_17')）
File "policy_test.py", line 33, in
r = open(args_path, 'rb')
FileNotFoundError: [Errno 2] No such file or directory: './policy_train/model_save/r4_17/r4_17'

Hello, I want to add some static obstacles to the dynamic obstacle world, so I modified the “dynamic_obs_test.yaml” file and modified it as follows. But when running, there are no static obstacles, please how should I set it up to run correctly.

When training to 250 epochs, I get the following error.

Could you help me see what the mistake might be?

Hi,

I follow the experiment in the README.md, the first stage is normal. However, after training in a circle scenario with 10 robots (python train_process_s2.py), the success rate is 0.00%. The experimental log is shown below:

....
time cost in one epoch 11.53249478340149 estimated remain time 0.009610412319501242 hours
current epoch 1998
The reward in this epoch: min [-81.33, -94.36, -91.05, -72.8, -132.44, -130.41, -156.77, -158.99, -124.27, -80.02] mean [-40.36, -56.27, -42.8, -50.67, -109.06, -70.3, -99.54, -83.21, -55.97, -49.13] max [-10.39, -35.73, -0.65, -26.63, -84.51, -13.68, -42.53, -23.88, -0.79, -28.84]
Early stopping at step 0 due to reaching max kl.
Early stopping at step 0 due to reaching max kl.
Early stopping at step 0 due to reaching max kl.
Early stopping at step 0 due to reaching max kl.
Early stopping at step 0 due to reaching max kl.
Early stopping at step 0 due to reaching max kl.
Early stopping at step 0 due to reaching max kl.
Early stopping at step 0 due to reaching max kl.
Early stopping at step 0 due to reaching max kl.
time cost in one epoch 11.119003295898438 estimated remain time 0.00617722405327691 hours
current epoch 1999
The reward in this epoch: min [-70.02, -77.01, -80.48, -62.08, -86.05, -113.78, -55.8, -77.18, -93.87, -111.82] mean [-50.08, -50.29, -50.64, -44.62, -43.91, -60.43, -39.81, -40.26, -59.32, -59.31] max [-31.91, -34.94, -24.68, -0.93, -0.76, -25.75, -17.74, -19.18, -37.56, -29.54]
Early stopping at step 0 due to reaching max kl.
Early stopping at step 0 due to reaching max kl.
Early stopping at step 0 due to reaching max kl.
Early stopping at step 0 due to reaching max kl.
Early stopping at step 0 due to reaching max kl.
Early stopping at step 0 due to reaching max kl.
Early stopping at step 0 due to reaching max kl.
Early stopping at step 0 due to reaching max kl.
Early stopping at step 0 due to reaching max kl.
time cost in one epoch 11.041501760482788 estimated remain time 0.00306708382235633 hours
current epoch 2000
The reward in this epoch: min [-67.57, -85.75, -105.89, -89.92, -103.05, -179.82, -113.64, -159.73, -124.8, -111.59] mean [-48.79, -51.79, -55.82, -50.35, -54.2, -71.38, -42.5, -52.12, -67.09, -56.98] max [-30.68, -27.21, -35.23, -26.13, -17.7, -0.68, -0.56, -0.53, -29.92, -29.35]
Policy Test Start !
Early stopping at step 0 due to reaching max kl.
Early stopping at step 0 due to reaching max kl.
Early stopping at step 0 due to reaching max kl.
Early stopping at step 0 due to reaching max kl.
Early stopping at step 0 due to reaching max kl.
Early stopping at step 0 due to reaching max kl.
Early stopping at step 0 due to reaching max kl.
Early stopping at step 0 due to reaching max kl.
Early stopping at step 0 due to reaching max kl.
time cost in one epoch 32.75892734527588 estimated remain time 0.0 hours
policy_name: r10_0_2000 successful rate: 0.00% average EpLen: 0 std length 0 average speed: 0.96 std speed 0.05