132 lines
5.7 KiB
Python
132 lines
5.7 KiB
Python
# SPDX-FileCopyrightText: Copyright (c) 2021 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
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# SPDX-License-Identifier: BSD-3-Clause
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#
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# Redistribution and use in source and binary forms, with or without
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# modification, are permitted provided that the following conditions are met:
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#
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# 1. Redistributions of source code must retain the above copyright notice, this
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# list of conditions and the following disclaimer.
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#
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# 2. Redistributions in binary form must reproduce the above copyright notice,
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# this list of conditions and the following disclaimer in the documentation
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# and/or other materials provided with the distribution.
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#
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# 3. Neither the name of the copyright holder nor the names of its
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# contributors may be used to endorse or promote products derived from
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# this software without specific prior written permission.
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#
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# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
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# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
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# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
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# DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
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# FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
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# DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
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# SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
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# CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
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# OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
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# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
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#
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# Copyright (c) 2021 ETH Zurich, Nikita Rudin
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import numpy as np
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import torch
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import torch.nn as nn
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from torch.distributions import Normal
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from torch.nn.modules import rnn
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from .actor_critic import ActorCritic, get_activation
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from rsl_rl.utils import unpad_trajectories
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class ActorCriticRecurrent(ActorCritic):
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is_recurrent = True
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def __init__(self, num_actor_obs,
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num_critic_obs,
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num_actions,
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actor_hidden_dims=[256, 256, 256],
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critic_hidden_dims=[256, 256, 256],
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activation='elu',
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rnn_type='lstm',
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rnn_hidden_size=256,
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rnn_num_layers=1,
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init_noise_std=1.0,
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**kwargs):
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super().__init__(num_actor_obs=rnn_hidden_size,
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num_critic_obs=rnn_hidden_size,
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num_actions=num_actions,
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actor_hidden_dims=actor_hidden_dims,
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critic_hidden_dims=critic_hidden_dims,
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activation=activation,
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init_noise_std=init_noise_std,
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**kwargs,
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)
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activation = get_activation(activation)
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self.velocity_planner = nn.Sequential(
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nn.Linear(num_actor_obs-3, 256),
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nn.ELU(),
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nn.Linear(256, 128),
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nn.ELU(),
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nn.Linear(128, 1),
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nn.Tanh()
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)
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self.lin_vel_x = kwargs["lin_vel_x"]
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self.memory_a = Memory(num_actor_obs, type=rnn_type, num_layers=rnn_num_layers, hidden_size=rnn_hidden_size)
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self.memory_c = Memory(num_critic_obs, type=rnn_type, num_layers=rnn_num_layers, hidden_size=rnn_hidden_size)
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print(f"Actor RNN: {self.memory_a}")
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print(f"Critic RNN: {self.memory_c}")
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def reset(self, dones=None):
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self.memory_a.reset(dones)
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self.memory_c.reset(dones)
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def act(self, observations, masks=None, hidden_states=None):
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vel_obs = torch.cat([observations[:, :9], observations[:, 12:]], dim=1)
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velocity = self.velocity_planner(vel_obs)
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velocity = torch.clip(velocity, self.lin_vel_x[0], self.lin_vel_x[1])
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self.velocity = velocity
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observations[:, 9] = velocity
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input_a = self.memory_a(observations, masks, hidden_states)
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return super().act(input_a.squeeze(0))
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def act_inference(self, observations):
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input_a = self.memory_a(observations)
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return super().act_inference(input_a.squeeze(0))
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def evaluate(self, critic_observations, masks=None, hidden_states=None):
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input_c = self.memory_c(critic_observations, masks, hidden_states)
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return super().evaluate(input_c.squeeze(0))
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def get_hidden_states(self):
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return self.memory_a.hidden_states, self.memory_c.hidden_states
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class Memory(torch.nn.Module):
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def __init__(self, input_size, type='lstm', num_layers=1, hidden_size=256):
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super().__init__()
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# RNN
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rnn_cls = nn.GRU if type.lower() == 'gru' else nn.LSTM
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self.rnn = rnn_cls(input_size=input_size, hidden_size=hidden_size, num_layers=num_layers)
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self.hidden_states = None
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def forward(self, input, masks=None, hidden_states=None):
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batch_mode = masks is not None
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if batch_mode:
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# batch mode (policy update): need saved hidden states
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if hidden_states is None:
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raise ValueError("Hidden states not passed to memory module during policy update")
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out, _ = self.rnn(input, hidden_states)
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out = unpad_trajectories(out, masks)
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else:
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# inference mode (collection): use hidden states of last step
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out, self.hidden_states = self.rnn(input.unsqueeze(0), self.hidden_states)
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return out
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def reset(self, dones=None):
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# When the RNN is an LSTM, self.hidden_states_a is a list with hidden_state and cell_state
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if self.hidden_states is None:
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return
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for hidden_state in self.hidden_states:
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hidden_state[..., dones, :] = 0.0 |