h1_task_button/h1taskbutton/actor_critic.py

# SPDX-FileCopyrightText: Copyright (c) 2021 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
# SPDX-License-Identifier: BSD-3-Clause
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# Copyright (c) 2021 ETH Zurich, Nikita Rudin

import numpy as np

import torch
import torch.nn as nn
from torch.distributions import Normal
from torch.nn.modules import rnn

class ActorCritic(nn.Module):
    is_recurrent = False
    def __init__(self,  num_actor_obs,
                        num_critic_obs,
                        num_actions,
                        obs_context_len=1, 
                        actor_hidden_dims=[256, 256, 256],
                        critic_hidden_dims=[256, 256, 256],
                        activation='elu',
                        init_noise_std=1.0,
                        **kwargs):
        if kwargs:
            print("ActorCritic.__init__ got unexpected arguments, which will be ignored: " + str([key for key in kwargs.keys()]))
        super(ActorCritic, self).__init__()
        
        self.obs_context_len = obs_context_len

        activation = get_activation(activation)

        mlp_input_dim_a = num_actor_obs
        mlp_input_dim_c = num_critic_obs

        # Policy
        actor_layers = []
        actor_layers.append(nn.Linear(mlp_input_dim_a, actor_hidden_dims[0]))
        actor_layers.append(activation)
        for l in range(len(actor_hidden_dims)):
            if l == len(actor_hidden_dims) - 1:
                actor_layers.append(nn.Linear(actor_hidden_dims[l], num_actions))
            else:
                actor_layers.append(nn.Linear(actor_hidden_dims[l], actor_hidden_dims[l + 1]))
                actor_layers.append(activation)
        self.actor = nn.Sequential(*actor_layers)

        # Value function
        critic_layers = []
        critic_layers.append(nn.Linear(mlp_input_dim_c, critic_hidden_dims[0]))
        critic_layers.append(activation)
        for l in range(len(critic_hidden_dims)):
            if l == len(critic_hidden_dims) - 1:
                critic_layers.append(nn.Linear(critic_hidden_dims[l], 1))
            else:
                critic_layers.append(nn.Linear(critic_hidden_dims[l], critic_hidden_dims[l + 1]))
                critic_layers.append(activation)
        self.critic = nn.Sequential(*critic_layers)

        print(f"Actor Network: {self.actor}")
        print(f"Critic Network: {self.critic}")

        # Action noise
        self.std = nn.Parameter(init_noise_std * torch.ones(num_actions))
        self.distribution = None
        # disable args validation for speedup
        Normal.set_default_validate_args = False
        
        # seems that we get better performance without init
        # self.init_memory_weights(self.memory_a, 0.001, 0.)
        # self.init_memory_weights(self.memory_c, 0.001, 0.)

    @staticmethod
    # not used at the moment
    def init_weights(sequential, scales):
        [torch.nn.init.orthogonal_(module.weight, gain=scales[idx]) for idx, module in
         enumerate(mod for mod in sequential if isinstance(mod, nn.Linear))]


    def reset(self, dones=None):
        pass

    def forward(self):
        raise NotImplementedError
    
    @property
    def action_mean(self):
        return self.distribution.mean

    @property
    def action_std(self):
        return self.distribution.stddev
    
    @property
    def entropy(self):
        return self.distribution.entropy().sum(dim=-1)

    def update_distribution(self, observations):
        mean = self.actor(observations)
        self.distribution = Normal(mean, mean*0. + self.std)

    def act(self, observations, **kwargs):
        if self.obs_context_len != 1:
            observations = observations[..., -1, :]
        self.update_distribution(observations)
        return self.distribution.sample()
    
    def get_actions_log_prob(self, actions):
        return self.distribution.log_prob(actions).sum(dim=-1)

    def act_inference(self, observations):
        if self.obs_context_len != 1:
            observations = observations[..., -1, :]
        actions_mean = self.actor(observations)
        return actions_mean
    
    def freeze(self):
        for param in self.parameters():
            param.requires_grad = False
        # self.eval()

    def evaluate(self, critic_observations, **kwargs):
        if self.obs_context_len != 1:
            critic_observations = critic_observations[..., -1, :]
        value = self.critic(critic_observations)
        return value

def get_activation(act_name):
    if act_name == "elu":
        return nn.ELU()
    elif act_name == "selu":
        return nn.SELU()
    elif act_name == "relu":
        return nn.ReLU()
    elif act_name == "crelu":
        return nn.ReLU()
    elif act_name == "lrelu":
        return nn.LeakyReLU()
    elif act_name == "tanh":
        return nn.Tanh()
    elif act_name == "sigmoid":
        return nn.Sigmoid()
    else:
        print("invalid activation function!")
        return None