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View Code? Open in Web Editor NEWA pytorch reprelication of the model-based reinforcement learning algorithm MBPO
A pytorch reprelication of the model-based reinforcement learning algorithm MBPO
我记得原版论文使用了多个SAC与ensemble_model,在咱们的代码里只发现了一个sac与model,是我记错了吗。。。
I found this line that causes the extreme slowdown in runtime (thousands times slower).
Line 11 in fe3c78c
Set to False returns to normal running speed, just a heads-up.
Hi,
Thank you for your code. It is really helpful.
Could you please check the line 115 in the main_mbpo.py? Since start_step will become larger and larger, if the condition is cur_step >= start_step + epoch_length, the truth epoch_length will also become larger and larger. So, is it a bug? Should we use
cur_step >= args.epoch_length
?
Correct me if I am wrong.
Thanks
Line 115 in 43c8a55
`
cur_step = total_step - start_step
if cur_step >= start_step + args.epoch_length and len(env_pool) > args.min_pool_size:
break
`
Hello,
Thanks for your awesome pytorch reimplementation! I'd like to have a try but I notice that I cannot find the utils in the main_mbpo.py file. May I have your help? Thanks!
Hi,
Really appreciate your reimplementation of MBPO with Pytorch!
However, there are several versions of TF and Pytorch, and the numpy versions they depend on are different to mujoco_py which will lead to a dependency conflict.
Will you add the requirements.txt of your environment and therefore i can reproduce the experiments? Thanks a lot!
Hey,
thank you for your work but sadly I'm not able to run your code I'm getting this inplace operation error. Weirdly that this only happens to me, I was just cloning the repo and running your example command.
File "mbpo.py", line 267, in
main()
File "mbpo.py", line 263, in main
train(args, env_sampler, predict_env, agent, env_pool, model_pool)
File "mbpo.py", line 124, in train
train_policy_steps += train_policy_repeats(args, total_step, train_policy_steps, cur_step, env_pool, model_pool, agent)
File "mbpo.py", line 220, in train_policy_repeats
agent.update_parameters((batch_state, batch_action, batch_reward, batch_next_state, batch_done), args.policy_train_batch_size, i
)
File "/shared/sebastian/replication-mbpo/sac/sac.py", line 89, in update_parameters
policy_loss.backward()
File "/shared/sebastian/miniconda3/envs/rrc_simulation/lib/python3.6/site-packages/torch/tensor.py", line 221, in backward
torch.autograd.backward(self, gradient, retain_graph, create_graph)
File "/shared/sebastian/miniconda3/envs/rrc_simulation/lib/python3.6/site-packages/torch/autograd/init.py", line 132, in backw
ard
allow_unreachable=True) # allow_unreachable flag
RuntimeError: one of the variables needed for gradient computation has been modified by an inplace operation: [torch.cuda.FloatTenso
r [256, 1]], which is output 0 of TBackward, is at version 3; expected version 2 instead. Hint: the backtrace further above shows th
e operation that failed to compute its gradient. The variable in question was changed in there or anywhere later. Good luck!
rollout_batch_size is default to 100k, which is what I don't understand? Does this mean even is real data is something like 5k, you still sample each data 20 times, and produce 100k data each time you call that function??
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