reagent.gym.policies.samplers package

Submodules

reagent.gym.policies.samplers.continuous_sampler module

class reagent.gym.policies.samplers.continuous_sampler.GaussianSampler(actor_network)

Bases: reagent.gym.types.Sampler

log_prob(scores: reagent.gym.types.GaussianSamplerScore, squashed_action: torch.Tensor) → torch.Tensor

sample_action(scores: reagent.gym.types.GaussianSamplerScore) → reagent.types.ActorOutput

reagent.gym.policies.samplers.discrete_sampler module

class reagent.gym.policies.samplers.discrete_sampler.EpsilonGreedyActionSampler(epsilon: float, epsilon_decay: float = 1.0, minimum_epsilon: float = 0.0)

Bases: reagent.gym.types.Sampler

Epsilon-Greedy Policy

With probability epsilon, a random action is sampled. Otherwise, the highest scoring (greedy) action is chosen.

Call update() to decay the amount of exploration by lowering epsilon by a factor of epsilon_decay (<=1) until we reach minimum_epsilon

log_prob(scores: torch.Tensor, action: torch.Tensor) → torch.Tensor

sample_action(scores: torch.Tensor) → reagent.types.ActorOutput

update() → None

Call to update internal parameters (e.g. decay epsilon)

class reagent.gym.policies.samplers.discrete_sampler.GreedyActionSampler

Bases: reagent.gym.types.Sampler

Return the highest scoring action.

log_prob(scores: torch.Tensor, action: torch.Tensor) → torch.Tensor

sample_action(scores: torch.Tensor) → reagent.types.ActorOutput

class reagent.gym.policies.samplers.discrete_sampler.SoftmaxActionSampler(temperature: float = 1.0)

Bases: reagent.gym.types.Sampler

Softmax sampler. Equation: http://incompleteideas.net/book/first/ebook/node17.html The action scores are logits.

Parameters

temperature – A measure of how uniformly random the distribution looks. The higher the temperature, the more uniform the sampling.

log_prob(scores: torch.Tensor, action: torch.Tensor) → torch.Tensor

sample_action(scores: torch.Tensor) → reagent.types.ActorOutput

reagent.gym.policies.samplers.top_k_sampler module

class reagent.gym.policies.samplers.top_k_sampler.TopKSampler(k: int)

Bases: reagent.gym.types.Sampler

log_prob(scores: torch.Tensor, action: torch.Tensor) → torch.Tensor

sample_action(scores: torch.Tensor) → reagent.types.ActorOutput

Module contents