torchrl.modules.mcts.EXP3Score

class torchrl.modules.mcts.EXP3Score(*args, **kwargs)

Computes action selection probabilities for the EXP3 algorithm in MCTS.

EXP3 (Exponential-weight algorithm for Exploration and Exploitation) is a bandit algorithm that performs well in adversarial or non-stationary environments. It maintains weights for each action and adjusts them based on received rewards.

Parameters:

• gamma (float, optional) – Exploration factor, balancing uniform exploration and exploitation of current weights. Must be in [0, 1]. Defaults to 0.1.

• weights_key (NestedKey, optional) – Key in the input TensorDictBase for the tensor containing current action weights. Defaults to “weights”.

• action_prob_key (NestedKey, optional) – Key to store the calculated action probabilities. Defaults to “action_prob”.

• score_key (NestedKey, optional) – Key where the calculated action probabilities will be stored. Defaults to “score”.

• num_actions_key (NestedKey, optional) – Key for the number of available actions (K). Defaults to “num_actions”.

Input Keys:

• weights_key (torch.Tensor): Tensor of shape (…, num_actions).

• num_actions_key (int or torch.Tensor): Scalar representing K, the number of actions.

Output Keys:

• score_key (torch.Tensor): Tensor of shape (…, num_actions) containing the calculated action probabilities.

Example

```python from tensordict import TensorDict from torchrl.modules.mcts.scores import EXP3Score

# Create an EXP3Score instance exp3 = EXP3Score(gamma=0.1)

# Define a TensorDict with required keys node = TensorDict(

{

“weights”: torch.tensor([1.0, 1.0]), “num_actions”: torch.tensor(2),

}, batch_size=[],

)

# Compute the action probabilities result = exp3(node) print(result[“score”]) # Output: Tensor with action probabilities ```

__init__(*, gamma: float = 0.1, weights_key: NestedKey = 'weights', action_prob_key: NestedKey = 'action_prob', reward_key: NestedKey = 'reward', score_key: NestedKey = 'score', num_actions_key: NestedKey = 'num_actions')

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

Methods

__init__(*[, gamma, weights_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.
update_weights(node, action_idx, reward)	Updates the weight of the chosen action based on the reward.
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
training