xhtshr / hw1 Goto Github PK

You can run this code on your own machine or on Google Colab.

1. Local option: If you choose to run locally, you will need to install MuJoCo and some Python packages; see installation.md for instructions.
2. Colab: The first few sections of the notebook will install all required dependencies. You can try out the Colab option by clicking the badge below:

Fill in sections marked with TODO. In particular, see

• infrastructure/rl_trainer.py
• policies/MLP_policy.py
• infrastructure/replay_buffer.py
• infrastructure/utils.py
• infrastructure/pytorch_util.py

Look for sections maked with HW1 to see how the edits you make will be used. Some other files that you may find relevant

• scripts/run_hw1.py (if running locally) or scripts/run_hw1.ipynb (if running on Colab)
• agents/bc_agent.py

See the homework pdf for more details.

Tip: While debugging, you probably want to keep the flag --video_log_freq -1 which will disable video logging and speed up the experiment. However, feel free to remove it to save videos of your awesome policy!

If running on Colab, adjust the #@params in the Args class according to the commmand line arguments above.

Command for problem 1:

python cs285/scripts/run_hw1.py \
	--expert_policy_file cs285/policies/experts/Ant.pkl \
	--env_name Ant-v2 --exp_name bc_ant --n_iter 1 \
	--expert_data cs285/expert_data/expert_data_Ant-v2.pkl
	--video_log_freq -1

Make sure to also try another environment. See the homework PDF for more details on what else you need to run. To generate videos of the policy, remove the --video_log_freq -1 flag.

Command for section 1: (Note the --do_dagger flag, and the higher value for n_iter)

python cs285/scripts/run_hw1.py \
    --expert_policy_file cs285/policies/experts/Ant.pkl \
    --env_name Ant-v2 --exp_name dagger_ant --n_iter 10 \
    --do_dagger --expert_data cs285/expert_data/expert_data_Ant-v2.pkl \
	--video_log_freq -1

Make sure to also try another environment. See the homework PDF for more details on what else you need to run.

You can visualize your runs using tensorboard:

tensorboard --logdir data

You will see scalar summaries as well as videos of your trained policies (in the 'images' tab).

You can choose to visualize specific runs with a comma-separated list:

tensorboard --logdir data/run1,data/run2,data/run3...

If running on Colab, you will be using the %tensorboard line magic to do the same thing; see the notebook for more details.

Lee?:
你用pytorch rsample 方法就可以实现 reparameterization trick

Lee?:
但是原理我觉得你还是要了解一下

伟大的蚊子:
式子看的好晕啊

Lee?:
不行就算了  能用就行

I dont know why, when I wanna save videos, I have to unset LD_PRELOAD

rollouts: 5