class DeterministicPolicyModel(RlModel):
""" Deterministic Policy Gradient - model """
def __init__(self, input_block: BackboneModel,
policy_backbone: LinearBackboneModel,
value_backbone: LinearBackboneModel, action_space: gym.Space):
super().__init__()
self.input_block = input_block
self.policy_backbone = policy_backbone
self.value_backbone = value_backbone
self.action_head = DeterministicActionHead(
self.policy_backbone.output_dim, action_space)
self.critic_head = DeterministicCriticHead(
self.value_backbone.output_dim)
def reset_weights(self):
""" Initialize properly model weights """
self.input_block.reset_weights()
self.policy_backbone.reset_weights()
self.value_backbone.reset_weights()
self.action_head.reset_weights()
self.critic_head.reset_weights()
def forward(self, observations, input_actions=None):
""" Calculate model outputs """
observations = self.input_block(observations)
if input_actions is not None:
actions = input_actions
value_input = torch.cat([observations, actions], dim=1)
value_hidden = self.value_backbone(value_input)
values = self.critic_head(value_hidden)
else:
policy_hidden = self.policy_backbone(observations)
actions = self.action_head(policy_hidden)
value_input = torch.cat([observations, actions], dim=1)
value_hidden = self.value_backbone(value_input)
values = self.critic_head(value_hidden)
return actions, values
def policy_parameters(self):
""" Parameters of policy """
return it.chain(self.policy_backbone.parameters(),
self.action_head.parameters())
def value_parameters(self):
""" Parameters of policy """
return it.chain(self.value_backbone.parameters(),
self.critic_head.parameters())
def get_layer_groups(self):
""" Return layers grouped """
return [
[self.policy_backbone, self.action_head],
[
self.value_backbone,
[
y for (x, y) in self.critic_head.named_parameters()
if x.endswith('bias')
]
],
# OpenAI regularizes only weight on the last layer. I'm just replicating that
[[
y for (x, y) in self.critic_head.named_parameters()
if x.endswith('weight')
]]
]
def step(self, observations):
""" Select actions based on model's output """
action, value = self(observations)
return {'actions': action, 'values': value}
def value(self, observation, input_actions=None):
""" Calculate value for given state """
action, value = self(observation, input_actions)
return value
def action(self, observations):
""" Calculate value for given state """
observations = self.input_block(observations)
policy_hidden = self.policy_backbone(observations)
action = self.action_head(policy_hidden)
return action
def evaluate(self, rollout: Rollout) -> Evaluator:
""" Evaluate model on a rollout """
return DeterministicPolicyEvaluator(self, rollout)
class DeterministicPolicyModel(Model):
""" Deterministic Policy Gradient - model """
def __init__(self, policy_backbone: LinearBackboneModel, value_backbone: LinearBackboneModel,
action_space: gym.Space):
super().__init__()
self.policy_backbone = policy_backbone
self.value_backbone = value_backbone
self.action_head = DeterministicActionHead(self.policy_backbone.output_dim, action_space)
self.critic_head = DeterministicCriticHead(self.value_backbone.output_dim, action_space)
def reset_weights(self):
""" Initialize properly model weights """
self.policy_backbone.reset_weights()
self.value_backbone.reset_weights()
self.action_head.reset_weights()
self.critic_head.reset_weights()
def forward(self, observations, input_actions=None):
""" Calculate model outputs """
observations = observations.float()
value_hidden = self.value_backbone(observations)
if input_actions is not None:
action = input_actions
value = self.critic_head(value_hidden, input_actions)
else:
policy_hidden = self.policy_backbone(observations)
action = self.action_head(policy_hidden)
value = self.critic_head(value_hidden, action)
return action, value
def policy_parameters(self):
""" Parameters of policy """
return it.chain(self.policy_backbone.parameters(), self.action_head.parameters())
def value_parameters(self):
""" Parameters of policy """
return it.chain(self.value_backbone.parameters(), self.critic_head.parameters())
def get_layer_groups(self):
""" Return layers grouped """
return [
[self.policy_backbone, self.action_head],
[self.value_backbone, self.critic_head]
]
def step(self, observation):
""" Select actions based on model's output """
action, value = self(observation)
return {
'actions': action,
'values': value
}
def value(self, observation, input_actions=None):
""" Calculate value for given state """
action, value = self(observation, input_actions)
return value
def action(self, observations):
""" Calculate value for given state """
observations = observations.float()
policy_hidden = self.policy_backbone(observations)
action = self.action_head(policy_hidden)
return action