torchrl.modules.mcts.PUCTScore

class torchrl.modules.mcts.PUCTScore(*args, **kwargs)

Computes the PUCT (Polynomial Upper Confidence Trees) score for MCTS.

PUCT is a widely used score in MCTS algorithms, notably in AlphaGo and AlphaZero, to balance exploration and exploitation. It incorporates prior probabilities from a policy network, encouraging exploration of actions deemed promising by the policy, while also considering visit counts and accumulated rewards.

The formula used is: score = (win_count / visits) + c * prior_prob * sqrt(total_visits) / (1 + visits)

Where: - win_count: Sum of rewards (or win counts) for the action. - visits: Visit count for the action. - total_visits: Visit count of the parent node (N). - prior_prob: Prior probability of selecting the action (e.g., from a policy network). - c: The exploration constant, controlling the trade-off between exploitation

(first term) and exploration (second term).

Parameters:

• c (float) – The exploration constant.

• win_count_key (NestedKey, optional) – Key for the tensor in the input TensorDictBase containing the sum of rewards (or win counts) for each action. Defaults to “win_count”.

• visits_key (NestedKey, optional) – Key for the tensor containing the visit count for each action. Defaults to “visits”.

• total_visits_key (NestedKey, optional) – Key for the tensor (or scalar) representing the visit count of the parent node (N). Defaults to “total_visits”.

• prior_prob_key (NestedKey, optional) – Key for the tensor containing the prior probabilities for each action. Defaults to “prior_prob”.

• score_key (NestedKey, optional) – Key where the calculated PUCT scores will be stored in the output TensorDictBase. Defaults to “score”.

Input Keys:

• win_count_key (torch.Tensor): Tensor of shape (…, num_actions) or matching visits_key.

• visits_key (torch.Tensor): Tensor of shape (…, num_actions). If an action has zero visits, its exploitation term (win_count / visits) will result in NaN if win_count is also zero, or +/-inf if win_count is non-zero. The exploration term will still be valid due to (1 + visits).

• total_visits_key (torch.Tensor): Scalar or tensor broadcastable to other inputs, representing the parent node’s visit count.

• prior_prob_key (torch.Tensor): Tensor of shape (…, num_actions) containing prior probabilities.

Output Keys:

• score_key (torch.Tensor): Tensor of the same shape as visits_key, containing the calculated PUCT scores.

Example

```python from tensordict import TensorDict from torchrl.modules.mcts.scores import PUCTScore

# Create a PUCTScore instance puct = PUCTScore(c=1.5)

# Define a TensorDict with required keys node = TensorDict(

{

“win_count”: torch.tensor([10.0, 20.0]), “visits”: torch.tensor([5.0, 10.0]), “total_visits”: torch.tensor(50.0), “prior_prob”: torch.tensor([0.6, 0.4]),

}, batch_size=[],

)

# Compute the PUCT scores result = puct(node) print(result[“score”]) # Output: Tensor with PUCT scores ```

__init__(*, c: float, win_count_key: NestedKey = 'win_count', visits_key: NestedKey = 'visits', total_visits_key: NestedKey = 'total_visits', prior_prob_key: NestedKey = 'prior_prob', score_key: NestedKey = 'score')

Initialize internal Module state, shared by both nn.Module and ScriptModule.

Methods

__init__(*, c[, win_count_key, visits_key, ...])	Initialize internal Module state, shared by both nn.Module and ScriptModule.
add_module(name, module)	Add a child module to the current module.
apply(fn)	Apply fn recursively to every submodule (as returned by .children()) as well as self.
bfloat16()	Casts all floating point parameters and buffers to bfloat16 datatype.
buffers([recurse])	Return an iterator over module buffers.
children()	Return an iterator over immediate children modules.
compile(*args, **kwargs)	Compile this Module's forward using torch.compile().
cpu()	Move all model parameters and buffers to the CPU.
cuda([device])	Move all model parameters and buffers to the GPU.
double()	Casts all floating point parameters and buffers to double datatype.
eval()	Set the module in evaluation mode.
extra_repr()	Return the extra representation of the module.
float()	Casts all floating point parameters and buffers to float datatype.
forward(node)	Define the computation performed at every call.
get_buffer(target)	Return the buffer given by target if it exists, otherwise throw an error.
get_extra_state()	Return any extra state to include in the module's state_dict.
get_parameter(target)	Return the parameter given by target if it exists, otherwise throw an error.
get_submodule(target)	Return the submodule given by target if it exists, otherwise throw an error.
half()	Casts all floating point parameters and buffers to half datatype.
ipu([device])	Move all model parameters and buffers to the IPU.
is_tdmodule_compatible(module)	Checks if a module is compatible with TensorDictModule API.
load_state_dict(state_dict[, strict, assign])	Copy parameters and buffers from state_dict into this module and its descendants.
modules()	Return an iterator over all modules in the network.
mtia([device])	Move all model parameters and buffers to the MTIA.
named_buffers([prefix, recurse, ...])	Return an iterator over module buffers, yielding both the name of the buffer as well as the buffer itself.
named_children()	Return an iterator over immediate children modules, yielding both the name of the module as well as the module itself.
named_modules([memo, prefix, remove_duplicate])	Return an iterator over all modules in the network, yielding both the name of the module as well as the module itself.
named_parameters([prefix, recurse, ...])	Return an iterator over module parameters, yielding both the name of the parameter as well as the parameter itself.
parameters([recurse])	Return an iterator over module parameters.
register_backward_hook(hook)	Register a backward hook on the module.
register_buffer(name, tensor[, persistent])	Add a buffer to the module.
register_forward_hook(hook, *[, prepend, ...])	Register a forward hook on the module.
register_forward_pre_hook(hook, *[, ...])	Register a forward pre-hook on the module.
register_full_backward_hook(hook[, prepend])	Register a backward hook on the module.
register_full_backward_pre_hook(hook[, prepend])	Register a backward pre-hook on the module.
register_load_state_dict_post_hook(hook)	Register a post-hook to be run after module's load_state_dict() is called.
register_load_state_dict_pre_hook(hook)	Register a pre-hook to be run before module's load_state_dict() is called.
register_module(name, module)	Alias for add_module().
register_parameter(name, param)	Add a parameter to the module.
register_state_dict_post_hook(hook)	Register a post-hook for the state_dict() method.
register_state_dict_pre_hook(hook)	Register a pre-hook for the state_dict() method.
requires_grad_([requires_grad])	Change if autograd should record operations on parameters in this module.
reset_out_keys()	Resets the out_keys attribute to its orignal value.
reset_parameters_recursive([parameters])	Recursively reset the parameters of the module and its children.
select_out_keys(*out_keys)	Selects the keys that will be found in the output tensordict.
set_extra_state(state)	Set extra state contained in the loaded state_dict.
set_submodule(target, module[, strict])	Set the submodule given by target if it exists, otherwise throw an error.
share_memory()	See torch.Tensor.share_memory_().
state_dict(*args[, destination, prefix, ...])	Return a dictionary containing references to the whole state of the module.
to(*args, **kwargs)	Move and/or cast the parameters and buffers.
to_empty(*, device[, recurse])	Move the parameters and buffers to the specified device without copying storage.
train([mode])	Set the module in training mode.
type(dst_type)	Casts all parameters and buffers to dst_type.
xpu([device])	Move all model parameters and buffers to the XPU.
zero_grad([set_to_none])	Reset gradients of all model parameters.

Attributes

T_destination
call_super_init
dump_patches
in_keys
out_keys
out_keys_source
c
training