The torch_ac package contains the PyTorch implementation of two Actor-Critic deep reinforcement learning algorithms:

• Synchronous A3C (A2C)
• Proximal Policy Optimizations (PPO)

Note: An example of use of this package is given in the rl-starter-files repository. More details below.

• Recurrent policies
• Reward shaping
• Handle observation spaces that are tensors or dict of tensors
• Handle discrete action spaces
• Observation preprocessing
• Multiprocessing
• CUDA

pip3 install torch-ac

Note: If you want to modify torch-ac algorithms, you will need to rather install a cloned version, i.e.:

git clone https://github.com/lcswillems/torch-ac.git
cd torch-ac
pip3 install -e .

A brief overview of the components of the package:

• torch_ac.A2CAlgo and torch_ac.PPOAlgo classes for A2C and PPO algorithms
• torch_ac.ACModel and torch_ac.RecurrentACModel abstract classes for non-recurrent and recurrent actor-critic models
• torch_ac.DictList class for making dictionnaries of lists list-indexable and hence batch-friendly

Here are detailled the most important components of the package.

torch_ac.A2CAlgo and torch_ac.PPOAlgo have 2 methods:

• __init__ that may take, among the other parameters:
  • an acmodel actor-critic model, i.e. an instance of a class inheriting from either torch_ac.ACModel or torch_ac.RecurrentACModel.
  • a preprocess_obss function that transforms a list of observations into a list-indexable object X (e.g. a PyTorch tensor). The default preprocess_obss function converts observations into a PyTorch tensor.
  • a reshape_reward function that takes into parameter an observation obs, the action action taken, the reward reward received and the terminal status done and returns a new reward. By default, the reward is not reshaped.
  • a recurrence number to specify over how many timesteps gradient is backpropagated. This number is only taken into account if a recurrent model is used and must divide the num_frames_per_agent parameter and, for PPO, the batch_size parameter.
• update_parameters that first collects experiences, then update the parameters and finally returns logs.

torch_ac.ACModel has 2 abstract methods:

• __init__ that takes into parameter an observation_space and an action_space.
• forward that takes into parameter N preprocessed observations obs and returns a PyTorch distribution dist and a tensor of values value. The tensor of values must be of size N, not N x 1.

torch_ac.RecurrentACModel has 3 abstract methods:

• __init__ that takes into parameter the same parameters than torch_ac.ACModel.
• forward that takes into parameter the same parameters than torch_ac.ACModel along with a tensor of N memories memory of size N x M where M is the size of a memory. It returns the same thing than torch_ac.ACModel plus a tensor of N memories memory.
• memory_size that returns the size M of a memory.

Note: The preprocess_obss function must return a list-indexable object (e.g. a PyTorch tensor). If your observations are dictionnaries, your preprocess_obss function may first convert a list of dictionnaries into a dictionnary of lists and then make it list-indexable using the torch_ac.DictList class as follow:

>>> d = DictList({"a": [[1, 2], [3, 4]], "b": [[5], [6]]})
>>> d.a
[[1, 2], [3, 4]]
>>> d[0]
DictList({"a": [1, 2], "b": [5]})

Note: if you use a RNN, you will need to set batch_first to True.

Examples of use of the package components are given in the rl-starter-scripts repository.

...

algo = torch_ac.PPOAlgo(envs, acmodel, args.frames_per_proc, args.discount, args.lr, args.gae_lambda,
                        args.entropy_coef, args.value_loss_coef, args.max_grad_norm, args.recurrence,
                        args.optim_eps, args.clip_eps, args.epochs, args.batch_size, preprocess_obss)

...

exps, logs1 = algo.collect_experiences()
logs2 = algo.update_parameters(exps)

More details here.

torch_ac.DictList({
    "image": preprocess_images([obs["image"] for obs in obss], device=device),
    "text": preprocess_texts([obs["mission"] for obs in obss], vocab, device=device)
})

More details here.

class ACModel(nn.Module, torch_ac.RecurrentACModel):
    ...

    def forward(self, obs, memory):
        ...

        return dist, value, memory

More details here.

More details here.