├── LICENSE ├── README.md ├── doc └── thumbnail.png ├── initial_buffer ├── __init__.py ├── algorithms │ ├── __init__.py │ ├── __pycache__ │ │ ├── __init__.cpython-312.pyc │ │ └── projection_buffer.cpython-312.pyc │ └── projection_buffer.py └── utils │ ├── __init__.py │ ├── __pycache__ │ ├── __init__.cpython-312.pyc │ └── loss.cpython-312.pyc │ └── loss.py ├── requirements.txt ├── setup.py └── toy_example.py /LICENSE: -------------------------------------------------------------------------------- 1 | GNU GENERAL PUBLIC LICENSE 2 | Version 3, 29 June 2007 3 | 4 | Copyright (C) 2007 Free Software Foundation, Inc. 5 | Everyone is permitted to copy and distribute verbatim copies 6 | of this license document, but changing it is not allowed. 7 | 8 | Preamble 9 | 10 | The GNU General Public License is a free, copyleft license for 11 | software and other kinds of works. 12 | 13 | The licenses for most software and other practical works are designed 14 | to take away your freedom to share and change the works. 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Interpretation of Sections 15 and 16. 613 | 614 | If the disclaimer of warranty and limitation of liability provided 615 | above cannot be given local legal effect according to their terms, 616 | reviewing courts shall apply local law that most closely approximates 617 | an absolute waiver of all civil liability in connection with the 618 | Program, unless a warranty or assumption of liability accompanies a 619 | copy of the Program in return for a fee. 620 | 621 | END OF TERMS AND CONDITIONS 622 | 623 | How to Apply These Terms to Your New Programs 624 | 625 | If you develop a new program, and you want it to be of the greatest 626 | possible use to the public, the best way to achieve this is to make it 627 | free software which everyone can redistribute and change under these terms. 628 | 629 | To do so, attach the following notices to the program. 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If not, see . 649 | 650 | Also add information on how to contact you by electronic and paper mail. 651 | 652 | If the program does terminal interaction, make it output a short 653 | notice like this when it starts in an interactive mode: 654 | 655 | Copyright (C) 656 | This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'. 657 | This is free software, and you are welcome to redistribute it 658 | under certain conditions; type `show c' for details. 659 | 660 | The hypothetical commands `show w' and `show c' should show the appropriate 661 | parts of the General Public License. Of course, your program's commands 662 | might be different; for a GUI interface, you would use an "about box". 663 | 664 | You should also get your employer (if you work as a programmer) or school, 665 | if any, to sign a "copyright disclaimer" for the program, if necessary. 666 | For more information on this, and how to apply and follow the GNU GPL, see 667 | . 668 | 669 | The GNU General Public License does not permit incorporating your program 670 | into proprietary programs. If your program is a subroutine library, you 671 | may consider it more useful to permit linking proprietary applications with 672 | the library. If this is what you want to do, use the GNU Lesser General 673 | Public License instead of this License. But first, please read 674 | . 675 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # Contrastive Initial State Buffer for Reinforcement Learning 2 | 3 |

4 | 5 | youtube_video 6 | 7 |

8 | 9 | This is the code for the ICRA24 paper **Contrastive Initial State Buffer for Reinforcement Learning** 10 | ([PDF](https://rpg.ifi.uzh.ch/docs/ICRA24_Messikommer.pdf)) by [Nico Messikommer](https://messikommernico.github.io/), [Yunlong Song](https://yun-long.github.io/), and [Davide Scaramuzza](http://rpg.ifi.uzh.ch/people_scaramuzza.html). 11 | For an overview of our method, check out our [video](https://youtu.be/RB7mDq2fhho). 12 | 13 | If you use any of this code, please cite the following publication: 14 | 15 | ```bibtex 16 | @Article{Messikommer24icra, 17 | author = {Nico Messikommer and Yunlong Song and Davide Scaramuzza}, 18 | title = {Contrastive Initial State Buffer for Reinforcement Learning}, 19 | journal = {2024 IEEE International Conference on Robotics and Automation (ICRA)}, 20 | year = {2024}, 21 | } 22 | ``` 23 | 24 | ## Abstract 25 | 26 | In Reinforcement Learning, the trade-off between exploration and exploitation poses a complex challenge for achieving efficient learning from limited samples. 27 | While recent works have been effective in leveraging past experiences for policy updates, they often overlook the potential of reusing past experiences for data collection. 28 | Independent of the underlying RL algorithm, we introduce the concept of a Contrastive Initial State Buffer, which strategically selects states from past experiences and uses them to initialize the agent in the environment in order to guide it toward more informative states. 29 | We validate our approach on two complex robotic tasks without relying on any prior information about the environment: (i) locomotion of a quadruped robot traversing challenging terrains and (ii) a quadcopter drone racing through a track. 30 | The experimental results show that our initial state buffer achieves higher task performance than the nominal baseline while also speeding up training convergence. 31 | 32 | ## Content 33 | 34 | This repository contains the code for the Contrastive Initial State Buffer (CL-Buffer), which can be installed as a Python library. 35 | The repository does not contain the code for training an RL agent in an environment (Drone Racing or Legged Locomotion). 36 | However, with the given toy example (```toy_example.py```), it is straightforward to implement the CL-Buffer in an existing RL framework. 37 | 38 | ## Installation 39 | 40 | 1. If desired, a conda environment can be created using the following command: 41 | 42 | ```bash 43 | conda create -n 44 | ``` 45 | 46 | 2. If needed, the dependencies for the ```toy_example.py``` script can be installed via the requirements.txt file. 47 | ```bash 48 | pip install -r requirements.txt 49 | ``` 50 |
51 | Dependencies: 52 |
    53 |
  • PyTorch
    • 54 |
  • Numpy
    • 55 |
  • Fast Pytorch Kmeans
    • 56 |

57 | 58 | 3. Install the CL-Buffer library by running the following command inside the directory where the ```setup.py``` file is located. 59 | ```bash 60 | pip install . 61 | ``` 62 | 63 | 64 | ## Usage 65 | Installing the initial state buffer as a library makes it possible to import the buffer using the import statement directly 66 | ```bash 67 | from initial_buffer.algorithms.projection_buffer import ProjectionBuffer 68 | ``` 69 | 70 | The ```ProjectionBuffer``` class includes three sampling methods: ['network', 'observations', 'random']. 71 | The CL-Buffer corresponds to the 'network' sampling strategy. 72 | For the explanation of the other sampling strategies, we refer to the paper. 73 | 74 | There are multiple hyperparameters that can be set for the training of the buffer; see the arguments in the ```__init__``` function for the ```ProjectionBuffer``` class in ```initial_buffer/algorithms/projection_buffer.py```. 75 | Generally, we noticed that the initial state clustering is not affected much by parameters in a similar range as the default parameters. 76 | 77 | For a toy example, please have a look at the ```toy_example.py``` script. 78 | It includes template functions for adding visited experiences to the buffer, training the buffer, and using the buffer for the selection of states. 79 | The visited state buffer is not included in the ```toy_example.py``` since it highly depends on the underlying environment. 80 | However, the visited state buffer can be implemented relatively easily using a simple array/dict/list storing the states, observations, dones, and rewards of the collected experiences. 81 | 82 | -------------------------------------------------------------------------------- /doc/thumbnail.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/uzh-rpg/cl_initial_buffer/47d20f9717eb9c3bc088278211980d5999413f4c/doc/thumbnail.png -------------------------------------------------------------------------------- /initial_buffer/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/uzh-rpg/cl_initial_buffer/47d20f9717eb9c3bc088278211980d5999413f4c/initial_buffer/__init__.py -------------------------------------------------------------------------------- /initial_buffer/algorithms/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/uzh-rpg/cl_initial_buffer/47d20f9717eb9c3bc088278211980d5999413f4c/initial_buffer/algorithms/__init__.py -------------------------------------------------------------------------------- /initial_buffer/algorithms/__pycache__/__init__.cpython-312.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/uzh-rpg/cl_initial_buffer/47d20f9717eb9c3bc088278211980d5999413f4c/initial_buffer/algorithms/__pycache__/__init__.cpython-312.pyc -------------------------------------------------------------------------------- /initial_buffer/algorithms/__pycache__/projection_buffer.cpython-312.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/uzh-rpg/cl_initial_buffer/47d20f9717eb9c3bc088278211980d5999413f4c/initial_buffer/algorithms/__pycache__/projection_buffer.cpython-312.pyc -------------------------------------------------------------------------------- /initial_buffer/algorithms/projection_buffer.py: -------------------------------------------------------------------------------- 1 | from typing import Union 2 | 3 | import torch 4 | import numpy as np 5 | from fast_pytorch_kmeans import KMeans as GPU_KMeans 6 | 7 | from initial_buffer.utils.loss import SoftNearestNeighborLoss 8 | 9 | 10 | class ProjectionBuffer: 11 | def __init__( 12 | self, 13 | device: str, 14 | obs_dim: int, 15 | advantage_gamma: float, 16 | gae_lambda: float, 17 | nr_clusters: int, 18 | sampling_strategy: str = 'network', 19 | cluster_algo: str = 'kmeans', 20 | lr_rate: float = 1e-4, 21 | min_timesteps: int = 16, 22 | n_train_data: int = 256, 23 | cluster_embedding_dim: int = 64, 24 | n_cluster_states: int = 256, 25 | nr_mining_samples: int = 10 26 | ): 27 | self.sampling_strategy = sampling_strategy 28 | 29 | self.advantage_gamma = advantage_gamma 30 | self.gae_lambda = gae_lambda 31 | self.obs_dim = obs_dim 32 | self.device = device 33 | self.lr_rate = lr_rate 34 | self.min_timesteps = min_timesteps 35 | self.n_train_data = n_train_data 36 | self.cluster_embedding_dim = cluster_embedding_dim 37 | self.n_cluster_states = n_cluster_states 38 | self.nr_mining_samples = nr_mining_samples 39 | self.nr_clusters = nr_clusters 40 | self.cluster_algo = cluster_algo 41 | 42 | self.train_data = None 43 | self.prev_improvement = None 44 | 45 | if self.sampling_strategy == 'network': 46 | self.setup_projection_network() 47 | 48 | def setup_projection_network(self) -> None: 49 | cluster_net_arch = [self.obs_dim, 64, 64] 50 | activation_fn = torch.nn.Tanh 51 | cluster_net = [] 52 | last_layer_dim = cluster_net_arch[0] 53 | for layer_dim_out in cluster_net_arch[1:]: 54 | cluster_net.append(torch.nn.Linear(last_layer_dim, layer_dim_out)) 55 | cluster_net.append(activation_fn()) 56 | last_layer_dim = layer_dim_out 57 | cluster_net.append(torch.nn.Linear(last_layer_dim, self.cluster_embedding_dim)) 58 | 59 | self.state_cluster_net = torch.nn.Sequential(*cluster_net).to(self.device) 60 | self.cluster_optimizer = torch.optim.Adam(self.state_cluster_net.parameters(), lr=self.lr_rate) 61 | self.cluster_contrastive_loss = SoftNearestNeighborLoss() 62 | 63 | def create_train_data(self, 64 | rollout_rewards: np.ndarray, 65 | rollout_observations: Union[torch.Tensor, np.ndarray], 66 | rollout_episode_starts: np.ndarray, 67 | ) -> None: 68 | """ 69 | 70 | :param rollout_rewards: shape of [nr_timesteps, nr_envs] 71 | :param rollout_observations: shape of [nr_timesteps, nr_envs, observ_dim] 72 | :param rollout_states: shape of [nr_timesteps, nr_envs, state_dim] 73 | :param rollout_episode_starts: shape of [nr_timesteps, nr_envs] 74 | :return: 75 | """ 76 | assert rollout_rewards.ndim == 2 77 | assert rollout_observations.ndim == 3 78 | assert rollout_episode_starts.ndim == 2 79 | assert self.sampling_strategy == 'network' 80 | 81 | nr_timesteps, nr_envs = rollout_rewards.shape 82 | observ_dim = rollout_observations.shape[-1] 83 | data_nr_timesteps = nr_timesteps - self.min_timesteps 84 | 85 | if type(rollout_observations) == np.ndarray: 86 | rollout_observations = torch.from_numpy(rollout_observations).to(self.device) 87 | 88 | # Reset previous improvement 89 | self.prev_improvement = None 90 | 91 | # Create arrays to store information 92 | cluster_rewards = np.zeros([self.n_train_data, data_nr_timesteps]) 93 | cluster_observs = torch.zeros([self.n_train_data, data_nr_timesteps, observ_dim], device=self.device) 94 | cluster_mask = np.zeros([self.n_train_data, data_nr_timesteps], dtype=bool) 95 | 96 | # Fill arrays 97 | index_idx = 0 98 | search_states_ids = np.random.permutation((nr_timesteps - self.min_timesteps) * nr_envs) 99 | for i_trajectory in range(self.n_train_data): 100 | # Find a subsequence with a minimum length of at least min_timesteps 101 | for _ in range(50): 102 | random_idx = search_states_ids[index_idx] 103 | idx_timestep = random_idx // nr_envs 104 | idx_envs = random_idx % nr_envs 105 | steps_next_start = rollout_episode_starts[idx_timestep:, idx_envs].argmax() 106 | 107 | index_idx = (index_idx + 1) % search_states_ids.shape[0] 108 | if steps_next_start > self.min_timesteps: 109 | break 110 | 111 | steps_next_start = min(steps_next_start, data_nr_timesteps) 112 | # If no environment restart, set next start to max possible index 113 | if steps_next_start == 0: 114 | steps_next_start = data_nr_timesteps - idx_timestep 115 | 116 | idx_trajectory_end = idx_timestep + steps_next_start 117 | cluster_rewards[i_trajectory, :steps_next_start] = rollout_rewards[idx_timestep:idx_trajectory_end, idx_envs] 118 | cluster_observs[i_trajectory, :steps_next_start, :] = rollout_observations[idx_timestep:idx_trajectory_end, 119 | idx_envs, :] 120 | cluster_mask[i_trajectory, :steps_next_start] = True 121 | 122 | # Crop the buffer 123 | max_timesteps = cluster_mask.sum(-1).max() 124 | self.train_data = { 125 | 'rewards': cluster_rewards[:, :max_timesteps], 126 | 'observs': cluster_observs[:, :max_timesteps, :], 127 | 'traj_mask': cluster_mask[:, :max_timesteps], 128 | } 129 | 130 | def get_train_sample_observs(self) -> np.ndarray: 131 | assert self.train_data is not None, 'Train data for initial state buffer has not been created before' 132 | return self.train_data['observs'][self.train_data['traj_mask']] 133 | 134 | def train_step(self, train_data_value: np.ndarray) -> float: 135 | assert train_data_value.ndim == 1 136 | assert self.sampling_strategy == 'network' 137 | 138 | improvement_begin_to_step = self.policy_improvement(train_data_value) 139 | if self.prev_improvement is None: 140 | self.prev_improvement = np.zeros_like(improvement_begin_to_step) 141 | 142 | gradient_improvement = improvement_begin_to_step - self.prev_improvement 143 | self.prev_improvement = improvement_begin_to_step 144 | 145 | pos_ids = np.argpartition(gradient_improvement, self.nr_mining_samples)[-self.nr_mining_samples:] 146 | neg_ids = np.argpartition(-gradient_improvement, self.nr_mining_samples)[-self.nr_mining_samples:] 147 | 148 | projection_loss = self.cluster_train_step(self.train_data['observs'][pos_ids, 0, :], 149 | self.train_data['observs'][neg_ids, 0, :]) 150 | 151 | return projection_loss 152 | 153 | def cluster_train_step(self, pos_observs: Union[torch.Tensor, np.ndarray], neg_observs: Union[torch.Tensor, np.ndarray]): 154 | net_input = torch.cat([pos_observs, neg_observs], dim=0) 155 | 156 | embs = self.project_obs(net_input) 157 | pos_embs, neg_embs = torch.split(embs, [pos_observs.shape[0], neg_observs.shape[0]]) 158 | 159 | loss = self.cluster_contrastive_loss(pos_embs[0, None, :], pos_embs[1:], neg_embs) 160 | 161 | self.cluster_optimizer.zero_grad() 162 | loss.backward() 163 | self.cluster_optimizer.step() 164 | 165 | return loss 166 | 167 | def policy_improvement(self, train_data_value: np.ndarray) -> np.ndarray: 168 | assert train_data_value.ndim == 1 169 | 170 | cluster_values = np.zeros_like(self.train_data['rewards']) 171 | cluster_values[self.train_data['traj_mask']] = train_data_value 172 | 173 | advantages = self.lambda_GAE_estimator(self.train_data['rewards'], 174 | cluster_values, 175 | np.logical_not(self.train_data['traj_mask']), 176 | self.train_data['traj_mask'], 177 | gamma=self.advantage_gamma, 178 | gae_lambda=self.gae_lambda) 179 | 180 | nr_timesteps = cluster_values.shape[1] 181 | gamma_t = self.advantage_gamma * np.ones(nr_timesteps) 182 | gamma_t[0] = 1 183 | gamma_t = np.cumprod(gamma_t) 184 | improvement = (advantages*gamma_t).sum(1) 185 | 186 | return improvement 187 | 188 | def create_initial_state_buffer( 189 | self, 190 | observations: Union[torch.Tensor, np.ndarray], 191 | buffer_length: int = 40, 192 | ) -> np.ndarray: 193 | """ 194 | 195 | :param observations: shape of [nr_samples, overv_dim] 196 | :return: 197 | """ 198 | if self.sampling_strategy == 'random': 199 | nr_samples = observations.shape[0] 200 | return np.random.permutation(nr_samples)[:buffer_length] 201 | 202 | assert observations.ndim == 2 203 | 204 | embs = self.project_obs(observations) 205 | cluster_distances, cluster_ids = self.cluster_embeddings(embs, 206 | nr_clusters=self.nr_clusters, 207 | cluster_algo=self.cluster_algo) 208 | k = buffer_length // self.nr_clusters 209 | k_smallest_distances_idx = np.argpartition(-cluster_distances, -k, axis=0)[-k:] 210 | k_smallest_distances = np.take_along_axis(cluster_distances, k_smallest_distances_idx, axis=0) 211 | sorted_idx = np.argsort(k_smallest_distances, axis=0) 212 | k_smallest_ordered_idx = np.take_along_axis(k_smallest_distances_idx, sorted_idx, axis=0) 213 | 214 | return k_smallest_ordered_idx.flatten() # Important: ordering 215 | 216 | def cluster_embeddings(self, embs, nr_clusters, cluster_algo='kmeans'): 217 | if cluster_algo == 'kmeans': 218 | kmeans = GPU_KMeans(n_clusters=nr_clusters, mode='cosine', verbose=1) 219 | _ = kmeans.fit_predict(embs) 220 | cluster_distances = 1 - torch.matmul(embs, (torch.nn.functional.normalize(kmeans.centroids, p=2)).T).detach().cpu().numpy() 221 | cluster_id = np.argmin(cluster_distances, axis=1) 222 | 223 | else: 224 | raise ValueError('Specified clustering algorithm is not implemented') 225 | 226 | return cluster_distances, cluster_id 227 | 228 | def project_obs(self, observs): 229 | if self.sampling_strategy == 'network': 230 | embs = self.state_cluster_net(observs) 231 | elif self.sampling_strategy == 'observations': 232 | embs = observs 233 | else: 234 | raise ValueError("Specified observation projection is not implemented") 235 | 236 | embs = torch.nn.functional.normalize(embs, dim=-1, p=2) 237 | 238 | return embs 239 | 240 | def lambda_GAE_estimator(self, 241 | rewards: np.ndarray, 242 | values: np.ndarray, 243 | episode_starts: np.ndarray, 244 | traj_mask: np.ndarray, 245 | gamma: float, 246 | gae_lambda: float) -> np.ndarray: 247 | """Adapted from stable_baselines3/common/buffers.py """ 248 | last_gae_lam = 0 249 | nr_cluster_states, buffer_size = rewards.shape[:2] 250 | advantages = np.zeros((nr_cluster_states, buffer_size), dtype=np.float32) 251 | 252 | for step in reversed(range(buffer_size - 1)): 253 | next_non_terminal = 1.0 - episode_starts[:, step + 1] 254 | next_values = values[:, step + 1] 255 | delta = rewards[:, step] + gamma * next_values * next_non_terminal - values[:, step] 256 | last_gae_lam = delta + gamma * gae_lambda * next_non_terminal * last_gae_lam 257 | advantages[:, step] = last_gae_lam 258 | # TD(lambda) estimator, see Github PR #375 or "Telescoping in TD(lambda)" 259 | # in David Silver Lecture 4: https://www.youtube.com/watch?v=PnHCvfgC_ZA 260 | 261 | advantages = advantages * traj_mask 262 | 263 | return advantages 264 | -------------------------------------------------------------------------------- /initial_buffer/utils/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/uzh-rpg/cl_initial_buffer/47d20f9717eb9c3bc088278211980d5999413f4c/initial_buffer/utils/__init__.py -------------------------------------------------------------------------------- /initial_buffer/utils/__pycache__/__init__.cpython-312.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/uzh-rpg/cl_initial_buffer/47d20f9717eb9c3bc088278211980d5999413f4c/initial_buffer/utils/__pycache__/__init__.cpython-312.pyc -------------------------------------------------------------------------------- /initial_buffer/utils/__pycache__/loss.cpython-312.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/uzh-rpg/cl_initial_buffer/47d20f9717eb9c3bc088278211980d5999413f4c/initial_buffer/utils/__pycache__/loss.cpython-312.pyc -------------------------------------------------------------------------------- /initial_buffer/utils/loss.py: -------------------------------------------------------------------------------- 1 | import torch 2 | from torch.nn import functional as F 3 | from torch.nn.modules.loss import _Loss 4 | 5 | 6 | class SoftNearestNeighborLoss(_Loss): 7 | def __init__(self, temperature=0.5) -> None: 8 | """ 9 | :param temperature: temperature for penalizing the negative embedding distance 10 | """ 11 | super().__init__() 12 | self.temperature = torch.tensor(temperature) 13 | 14 | @staticmethod 15 | def _cosine_similarity(x: torch.Tensor, y: torch.Tensor) -> torch.Tensor: 16 | """ 17 | Compute the cosine similarity between x and y, the range is [-1, 1] 18 | 19 | Args: 20 | x: input tensor 21 | y: input tensor 22 | """ 23 | return F.cosine_similarity(F.normalize(x, p=2), F.normalize(y, p=2)) 24 | 25 | def forward(self, input_batch: torch.Tensor, pos: torch.Tensor, neg: torch.Tensor) -> torch.Tensor: 26 | """ 27 | Args: 28 | input_batch: input batch 29 | pos: positive sample 30 | neg: negative sample 31 | pos_similarity: positive similarity 32 | neg_similarity: negative similarity 33 | """ 34 | assert input_batch.dim() == 2 35 | assert pos.dim() == 2 36 | assert neg.dim() == 2 37 | 38 | if self.temperature.device != input_batch.device: 39 | self.temperature = self.temperature.to(input_batch.device) 40 | 41 | num_similarity = F.cosine_similarity(F.normalize(input_batch, p=2), F.normalize(pos, p=2)) 42 | den_similarity = F.cosine_similarity(F.normalize(input_batch, p=2), F.normalize(neg, p=2)) 43 | 44 | num = torch.mean(torch.exp(num_similarity / self.temperature), dim=0) 45 | den = torch.mean(torch.exp(den_similarity / self.temperature), dim=0) + num 46 | 47 | loss = -torch.log(num / den) 48 | 49 | return loss.mean() 50 | -------------------------------------------------------------------------------- /requirements.txt: -------------------------------------------------------------------------------- 1 | # This file may be used to create an environment using: 2 | # $ conda create --name --file 3 | # platform: linux-64 4 | _libgcc_mutex=0.1=main 5 | _openmp_mutex=5.1=1_gnu 6 | blas=1.0=mkl 7 | bzip2=1.0.8=h7b6447c_0 8 | ca-certificates=2023.12.12=h06a4308_0 9 | certifi=2024.2.2=py312h06a4308_0 10 | charset-normalizer=2.0.4=pyhd3eb1b0_0 11 | cuda-cudart=12.1.105=0 12 | cuda-cupti=12.1.105=0 13 | cuda-libraries=12.1.0=0 14 | cuda-nvrtc=12.1.105=0 15 | cuda-nvtx=12.1.105=0 16 | cuda-opencl=12.3.101=0 17 | cuda-runtime=12.1.0=0 18 | expat=2.5.0=h6a678d5_0 19 | fast-pytorch-kmeans=0.2.0.1=pypi_0 20 | ffmpeg=4.3=hf484d3e_0 21 | filelock=3.13.1=py312h06a4308_0 22 | freetype=2.12.1=h4a9f257_0 23 | fsspec=2024.2.0=pypi_0 24 | gmp=6.2.1=h295c915_3 25 | gnutls=3.6.15=he1e5248_0 26 | idna=3.4=py312h06a4308_0 27 | initial-buffer=1.0.0=pypi_0 28 | intel-openmp=2023.1.0=hdb19cb5_46306 29 | jinja2=3.1.3=py312h06a4308_0 30 | jpeg=9e=h5eee18b_1 31 | lame=3.100=h7b6447c_0 32 | lcms2=2.12=h3be6417_0 33 | ld_impl_linux-64=2.38=h1181459_1 34 | lerc=3.0=h295c915_0 35 | libcublas=12.1.0.26=0 36 | libcufft=11.0.2.4=0 37 | libcufile=1.8.1.2=0 38 | libcurand=10.3.4.107=0 39 | libcusolver=11.4.4.55=0 40 | libcusparse=12.0.2.55=0 41 | libdeflate=1.17=h5eee18b_1 42 | libffi=3.4.4=h6a678d5_0 43 | libgcc-ng=11.2.0=h1234567_1 44 | libgomp=11.2.0=h1234567_1 45 | libiconv=1.16=h7f8727e_2 46 | libidn2=2.3.4=h5eee18b_0 47 | libjpeg-turbo=2.0.0=h9bf148f_0 48 | libnpp=12.0.2.50=0 49 | libnvjitlink=12.1.105=0 50 | libnvjpeg=12.1.1.14=0 51 | libpng=1.6.39=h5eee18b_0 52 | libstdcxx-ng=11.2.0=h1234567_1 53 | libtasn1=4.19.0=h5eee18b_0 54 | libtiff=4.5.1=h6a678d5_0 55 | libunistring=0.9.10=h27cfd23_0 56 | libuuid=1.41.5=h5eee18b_0 57 | libwebp-base=1.3.2=h5eee18b_0 58 | llvm-openmp=14.0.6=h9e868ea_0 59 | lz4-c=1.9.4=h6a678d5_0 60 | markupsafe=2.1.3=py312h5eee18b_0 61 | mkl=2023.1.0=h213fc3f_46344 62 | mkl-service=2.4.0=py312h5eee18b_1 63 | mkl_fft=1.3.8=py312h5eee18b_0 64 | mkl_random=1.2.4=py312hdb19cb5_0 65 | mpmath=1.3.0=py312h06a4308_0 66 | ncurses=6.4=h6a678d5_0 67 | nettle=3.7.3=hbbd107a_1 68 | networkx=3.1=py312h06a4308_0 69 | numpy=1.26.3=py312hc5e2394_0 70 | numpy-base=1.26.3=py312h0da6c21_0 71 | openh264=2.1.1=h4ff587b_0 72 | openjpeg=2.4.0=h3ad879b_0 73 | openssl=3.0.13=h7f8727e_0 74 | pillow=10.2.0=py312h5eee18b_0 75 | pip=23.3.1=py312h06a4308_0 76 | pynvml=11.5.0=pypi_0 77 | python=3.12.1=h996f2a0_0 78 | pytorch=2.2.0=py3.12_cuda12.1_cudnn8.9.2_0 79 | pytorch-cuda=12.1=ha16c6d3_5 80 | pytorch-mutex=1.0=cuda 81 | pyyaml=6.0.1=py312h5eee18b_0 82 | readline=8.2=h5eee18b_0 83 | requests=2.31.0=py312h06a4308_1 84 | setuptools=68.2.2=py312h06a4308_0 85 | sqlite=3.41.2=h5eee18b_0 86 | sympy=1.12=py312h06a4308_0 87 | tbb=2021.8.0=hdb19cb5_0 88 | tk=8.6.12=h1ccaba5_0 89 | torchaudio=2.2.0=py312_cu121 90 | torchvision=0.17.0=py312_cu121 91 | typing_extensions=4.9.0=py312h06a4308_1 92 | tzdata=2023d=h04d1e81_0 93 | urllib3=2.1.0=py312h06a4308_0 94 | wheel=0.41.2=py312h06a4308_0 95 | xz=5.4.5=h5eee18b_0 96 | yaml=0.2.5=h7b6447c_0 97 | zlib=1.2.13=h5eee18b_0 98 | zstd=1.5.5=hc292b87_0 99 | -------------------------------------------------------------------------------- /setup.py: -------------------------------------------------------------------------------- 1 | from setuptools import setup, find_packages 2 | 3 | setup(name='initial_buffer', 4 | version='1.0.0', 5 | author='Nico & Yunlong', 6 | author_email='nmessi@ifi.uzh.ch', 7 | license="BSD-3-Clause", 8 | packages=find_packages(), 9 | description='Initial state buffer to speed up training convergence', 10 | python_requires='>=3.6', 11 | install_requires=[ 12 | "torch>=1.4.0", 13 | "torchvision>=0.5.0", 14 | "numpy>=1.16.4" 15 | ], 16 | ) 17 | -------------------------------------------------------------------------------- /toy_example.py: -------------------------------------------------------------------------------- 1 | import torch 2 | import numpy as np 3 | 4 | from initial_buffer.algorithms.projection_buffer import ProjectionBuffer 5 | 6 | 7 | def evaluate_value_funtion(obs): 8 | return np.random.random([obs.shape[0]]) 9 | 10 | 11 | def main(): 12 | obs_dim = 18 13 | state_dim = 52 14 | ppo_gamma = 0.99 15 | gae_lambda = 0.95 16 | nr_timesteps = 250 17 | nr_envs = 8 18 | sampling_strategy = 'network' # ['network', 'observations', 'random'] 19 | device = 'cuda:0' 20 | 21 | projection_buffer = ProjectionBuffer( 22 | device=device, 23 | nr_clusters=64, 24 | cluster_algo='kmeans', 25 | obs_dim=obs_dim, 26 | advantage_gamma=ppo_gamma, 27 | gae_lambda=gae_lambda, 28 | sampling_strategy=sampling_strategy, 29 | min_timesteps=8, 30 | ) 31 | visited_state_buffer_obs = None 32 | for i_epoch in range(10): 33 | # ========================== Roll Out Phase ========================== 34 | # Select states to initialize robot in environment based on the states and observations stored in the visited state buffer 35 | if visited_state_buffer_obs is not None: 36 | selected_idx = projection_buffer.create_initial_state_buffer( 37 | torch.from_numpy(visited_state_buffer_obs.reshape([nr_timesteps*nr_envs, obs_dim])).to(device), 38 | buffer_length=256, 39 | ) 40 | initialization_states = visited_state_buffer_states.reshape([nr_timesteps*nr_envs, state_dim])[selected_idx] 41 | 42 | # Collect rollout data 43 | # Add experiences to visited state buffer / Here, the prefiltering to exclude failing states can be added 44 | visited_state_buffer_obs = np.random.random([nr_timesteps, nr_envs, obs_dim]).astype(dtype=np.float32) 45 | visited_state_buffer_rewards = np.random.random([nr_timesteps, nr_envs]) 46 | visited_state_buffer_dones = np.random.random([nr_timesteps, nr_envs]) 47 | visited_state_buffer_states = np.random.random([nr_timesteps, nr_envs, state_dim]) 48 | 49 | 50 | # ======================== Policy Update Phase ======================== 51 | projection_buffer.create_train_data( 52 | rollout_rewards=visited_state_buffer_rewards, 53 | rollout_observations=visited_state_buffer_obs, 54 | rollout_episode_starts=visited_state_buffer_dones, 55 | ) 56 | 57 | mean_cluster_loss = 0 58 | for i_policy_update in range(10): 59 | ## For each gradient update 60 | obs = projection_buffer.get_train_sample_observs() 61 | # The current value of the observations needs to be sampled at this points, e.g, actor_critic.evaluate(obs).squeeze(-1) 62 | # The following function is just a placeholder 63 | values = evaluate_value_funtion(obs) 64 | mean_cluster_loss += projection_buffer.train_step(values) 65 | 66 | 67 | if __name__ == '__main__': 68 | main() 69 | --------------------------------------------------------------------------------