0xangelo / raylab

Reinforcement learning algorithms in RLlib

Hi there 👋

raylab's Issues

Treat batch and sample shape dimensions in Reservoir's `transition_fn`

https://github.com/angelolovatto/raylab/blob/77b6a4ccea1e507cac2fc67bfb657192c44e26af/raylab/envs/reservoir.py#L83-L89

Our current implementation of Reservoir only samples one Gamma random variable per reservoir in the config. However, we should sample batches of rain variables inputting batches of states int the transition_fn. It does not crash currently because of broadcasting rules, as we add the rain samples to the batch of states. However, that shares random variables across batches of transitions, which is not ideal.

Furthermore, we're calculating the log-likelihood as if state and batch dimensions were uncorrelated, yielding a B x N matrix logp, where B is the batch size and N is the state dimension. We should use PyTorch's Independent distribution to squeeze logp across the state dimension, which isn't hard to do. An example can be found in raylab.distributions.DiagMultivariateNormal. I'll get right into it.

A last concern is about the nature of the log-likelihood itself. Even though rain is the only random variable involved in the transition, it is not clear that the transition's logp is the logp of the rain variable only. Although, since all subsequent modifications to the rain variable are additive, I assume that the change of variables theorem would give us the same pdf as the original rain variable anyway. @thiagopbueno, thoughts?

Running raylab experiment crashes

• raylab version: 0.14.14
• Python version: 3.7.9
• Operating System: Ubuntu 18.04.5 LTS

When running raylab experiment "PG" --config examples/PG/cartpole_defaults.py I get the following error:

Traceback (most recent call last):
  File "/home/palenicek/miniconda3/envs/raylab_test/bin/raylab", line 8, in <module>
    sys.exit(raylab())
  File "/home/palenicek/miniconda3/envs/raylab_test/lib/python3.7/site-packages/click/core.py", line 829, in __call__
    return self.main(*args, **kwargs)
  File "/home/palenicek/miniconda3/envs/raylab_test/lib/python3.7/site-packages/click/core.py", line 782, in main
    rv = self.invoke(ctx)
  File "/home/palenicek/miniconda3/envs/raylab_test/lib/python3.7/site-packages/click/core.py", line 1259, in invoke
    return _process_result(sub_ctx.command.invoke(sub_ctx))
  File "/home/palenicek/miniconda3/envs/raylab_test/lib/python3.7/site-packages/click/core.py", line 1066, in invoke
    return ctx.invoke(self.callback, **ctx.params)
  File "/home/palenicek/miniconda3/envs/raylab_test/lib/python3.7/site-packages/click/core.py", line 610, in invoke
    return callback(*args, **kwargs)
  File "/home/palenicek/miniconda3/envs/raylab_test/lib/python3.7/site-packages/raylab/cli/utils.py", line 164, in wrapped
    return func(*args, tune_kwargs=tune_kwargs, **kwargs)
  File "/home/palenicek/miniconda3/envs/raylab_test/lib/python3.7/site-packages/raylab/cli/utils.py", line 19, in wrapped
    return func(*args, **kwargs)
  File "/home/palenicek/miniconda3/envs/raylab_test/lib/python3.7/site-packages/click/decorators.py", line 21, in new_func
    return f(get_current_context(), *args, **kwargs)
  File "/home/palenicek/miniconda3/envs/raylab_test/lib/python3.7/site-packages/raylab/cli/utils.py", line 51, in wrapped
    process_tune_kwargs(ctx, **tune_kwargs)
  File "/home/palenicek/miniconda3/envs/raylab_test/lib/python3.7/site-packages/raylab/cli/utils.py", line 172, in process_tune_kwargs
    delete_if_necessary(ctx, osp.join(local_dir, name))
  File "/home/palenicek/miniconda3/envs/raylab_test/lib/python3.7/posixpath.py", line 94, in join
    genericpath._check_arg_types('join', a, *p)
  File "/home/palenicek/miniconda3/envs/raylab_test/lib/python3.7/genericpath.py", line 153, in _check_arg_types
    (funcname, s.__class__.__name__)) from None
TypeError: join() argument must be str or bytes, not 'NoneType'

I tried installing ray lab both using pip, as well as from source master and the last stable release version 0.10.0. I always get this error.

I am not sure, what the problem is. From the documentation I am also not 100% sure on how raylab should be used. I tried running the cli commands, as well as the /example files directly. All of which gave me the above error.

Any ideas on how to fix this issue?

Add documentation to HVAC and Reservoir environments

The title is self-explanatory. The specific classes are the ones below. It would be nice for users to understand these problems without diving too deeply into the code.

https://github.com/angelolovatto/raylab/blob/b73aa99c1dd4061bb9ded02c1de4800158c12c7a/raylab/envs/hvac.py#L33

https://github.com/angelolovatto/raylab/blob/b73aa99c1dd4061bb9ded02c1de4800158c12c7a/raylab/envs/reservoir.py#L32

@thiagopbueno, perhaps you have some description of these tasks lying around?

Scale tril by desired average action stddev

Currently we use a very ad-hoc procedure for scaling the quadratic component of NAF when used for exploration:
https://github.com/angelolovatto/raylab/blob/9820275b17ee085e1955a6d845c0bdf61333f8da/raylab/algorithms/naf/naf_policy.py#L150-L155

A possibly better alternative would be to scale it based on the desired average action stddev. Something like:

scale_tril * (1.0 / average_sttdev(scale_tril)) * desired_avg_stddev

That way we could contain the magnitude of the MultivariateNormal precision matrix while still allowing some freedom for diversifying the action's covariances.

Depfu Error: No dependency files found

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