cadrl_ros
cadrl_ros copied to clipboard
mit-acl
ROS package for dynamic obstacle avoidance for ground robots trained with deep RL
Hello, thanks for sharing your awesome work. I'm a beginner about this field and this is the first time I learn about it. I have been troubled by the above...
Hi, sorry to bother you again. I find the test cases (**preset_testCases**, num_agents:10) you used. I run again using the checkpoint file([gym-collision-avoidance](https://github.com/mit-acl/gym-collision-avoidance)/[gym_collision_avoidance](https://github.com/mit-acl/gym-collision-avoidance/tree/release/gym_collision_avoidance)/[envs](https://github.com/mit-acl/gym-collision-avoidance/tree/release/gym_collision_avoidance/envs)/[policies](https://github.com/mit-acl/gym-collision-avoidance/tree/release/gym_collision_avoidance/envs/policies)/[GA3C_CADRL](https://github.com/mit-acl/gym-collision-avoidance/tree/release/gym_collision_avoidance/envs/policies/GA3C_CADRL)/[checkpoints](https://github.com/mit-acl/gym-collision-avoidance/tree/release/gym_collision_avoidance/envs/policies/GA3C_CADRL/checkpoints)/IROS18/network_01900000) you provided with vpref=1.0, DT=0.2. But it fails....
Hi, I find the results in your paper (GA3C-CADRL) are pretty good. And it surprises me that the results in Fig.6 (b)(c) is so perfect.  It seems that these...