Commit
•
a6507cb
1
Parent(s):
960620e
pushing model
Browse files- README.md +69 -0
- c51.cleanrl_model +0 -0
- c51.py +278 -0
- events.out.tfevents.1672465429.fedora.998969.0 +3 -0
- poetry.lock +0 -0
- pyproject.toml +158 -0
- replay.mp4 +0 -0
- videos/CartPole-v1__c51__1__1672465429-eval/rl-video-episode-0.mp4 +0 -0
- videos/CartPole-v1__c51__1__1672465429-eval/rl-video-episode-1.mp4 +0 -0
- videos/CartPole-v1__c51__1__1672465429-eval/rl-video-episode-8.mp4 +0 -0
README.md
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---
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tags:
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- CartPole-v1
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- deep-reinforcement-learning
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- reinforcement-learning
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- custom-implementation
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library_name: cleanrl
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model-index:
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- name: C51
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results:
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- task:
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type: reinforcement-learning
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name: reinforcement-learning
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dataset:
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name: CartPole-v1
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type: CartPole-v1
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metrics:
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- type: mean_reward
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value: 500.00 +/- 0.00
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name: mean_reward
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verified: false
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---
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# (CleanRL) **C51** Agent Playing **CartPole-v1**
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This is a trained model of a C51 agent playing CartPole-v1.
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The model was trained by using [CleanRL](https://github.com/vwxyzjn/cleanrl) and the most up-to-date training code can be
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found [here](https://github.com/vwxyzjn/cleanrl/blob/master/cleanrl/c51.py).
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## Command to reproduce the training
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```bash
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curl -OL https://huggingface.co/cleanrl/CartPole-v1-c51-seed1/raw/main/c51.py
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curl -OL https://huggingface.co/cleanrl/CartPole-v1-c51-seed1/raw/main/pyproject.toml
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curl -OL https://huggingface.co/cleanrl/CartPole-v1-c51-seed1/raw/main/poetry.lock
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poetry install --all-extras
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python c51.py --save-model --upload-model --hf-entity cleanrl --cuda False --env-id CartPole-v1
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```
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# Hyperparameters
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```python
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{'batch_size': 128,
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'buffer_size': 10000,
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'capture_video': False,
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'cuda': False,
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'end_e': 0.05,
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'env_id': 'CartPole-v1',
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'exp_name': 'c51',
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'exploration_fraction': 0.5,
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'gamma': 0.99,
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'hf_entity': 'cleanrl',
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'learning_rate': 0.00025,
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'learning_starts': 10000,
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'n_atoms': 101,
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'save_model': True,
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'seed': 1,
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'start_e': 1,
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'target_network_frequency': 500,
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'torch_deterministic': True,
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'total_timesteps': 500000,
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'track': False,
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'train_frequency': 10,
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'upload_model': True,
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'v_max': 100,
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'v_min': -100,
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'wandb_entity': None,
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'wandb_project_name': 'cleanRL'}
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```
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c51.cleanrl_model
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Binary file (115 kB). View file
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c51.py
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# docs and experiment results can be found at https://docs.cleanrl.dev/rl-algorithms/c51/#c51py
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import argparse
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import os
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import random
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import time
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from distutils.util import strtobool
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import gym
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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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import torch.optim as optim
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from stable_baselines3.common.buffers import ReplayBuffer
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from torch.utils.tensorboard import SummaryWriter
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def parse_args():
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# fmt: off
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parser = argparse.ArgumentParser()
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parser.add_argument("--exp-name", type=str, default=os.path.basename(__file__).rstrip(".py"),
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help="the name of this experiment")
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parser.add_argument("--seed", type=int, default=1,
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help="seed of the experiment")
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parser.add_argument("--torch-deterministic", type=lambda x: bool(strtobool(x)), default=True, nargs="?", const=True,
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help="if toggled, `torch.backends.cudnn.deterministic=False`")
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parser.add_argument("--cuda", type=lambda x: bool(strtobool(x)), default=True, nargs="?", const=True,
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help="if toggled, cuda will be enabled by default")
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parser.add_argument("--track", type=lambda x: bool(strtobool(x)), default=False, nargs="?", const=True,
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help="if toggled, this experiment will be tracked with Weights and Biases")
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parser.add_argument("--wandb-project-name", type=str, default="cleanRL",
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help="the wandb's project name")
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parser.add_argument("--wandb-entity", type=str, default=None,
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help="the entity (team) of wandb's project")
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parser.add_argument("--capture-video", type=lambda x: bool(strtobool(x)), default=False, nargs="?", const=True,
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help="whether to capture videos of the agent performances (check out `videos` folder)")
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parser.add_argument("--save-model", type=lambda x: bool(strtobool(x)), default=False, nargs="?", const=True,
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help="whether to save model into the `runs/{run_name}` folder")
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parser.add_argument("--upload-model", type=lambda x: bool(strtobool(x)), default=False, nargs="?", const=True,
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help="whether to upload the saved model to huggingface")
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parser.add_argument("--hf-entity", type=str, default="",
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help="the user or org name of the model repository from the Hugging Face Hub")
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# Algorithm specific arguments
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parser.add_argument("--env-id", type=str, default="CartPole-v1",
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help="the id of the environment")
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parser.add_argument("--total-timesteps", type=int, default=500000,
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help="total timesteps of the experiments")
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parser.add_argument("--learning-rate", type=float, default=2.5e-4,
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help="the learning rate of the optimizer")
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parser.add_argument("--n-atoms", type=int, default=101,
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help="the number of atoms")
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parser.add_argument("--v-min", type=float, default=-100,
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help="the number of atoms")
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parser.add_argument("--v-max", type=float, default=100,
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help="the number of atoms")
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parser.add_argument("--buffer-size", type=int, default=10000,
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help="the replay memory buffer size")
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58 |
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parser.add_argument("--gamma", type=float, default=0.99,
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help="the discount factor gamma")
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parser.add_argument("--target-network-frequency", type=int, default=500,
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help="the timesteps it takes to update the target network")
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parser.add_argument("--batch-size", type=int, default=128,
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help="the batch size of sample from the reply memory")
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parser.add_argument("--start-e", type=float, default=1,
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help="the starting epsilon for exploration")
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parser.add_argument("--end-e", type=float, default=0.05,
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help="the ending epsilon for exploration")
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parser.add_argument("--exploration-fraction", type=float, default=0.5,
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help="the fraction of `total-timesteps` it takes from start-e to go end-e")
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parser.add_argument("--learning-starts", type=int, default=10000,
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help="timestep to start learning")
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parser.add_argument("--train-frequency", type=int, default=10,
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help="the frequency of training")
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args = parser.parse_args()
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# fmt: on
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return args
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def make_env(env_id, seed, idx, capture_video, run_name):
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def thunk():
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env = gym.make(env_id)
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env = gym.wrappers.RecordEpisodeStatistics(env)
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if capture_video:
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if idx == 0:
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env = gym.wrappers.RecordVideo(env, f"videos/{run_name}")
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env.seed(seed)
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env.action_space.seed(seed)
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env.observation_space.seed(seed)
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return env
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return thunk
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# ALGO LOGIC: initialize agent here:
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class QNetwork(nn.Module):
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def __init__(self, env, n_atoms=101, v_min=-100, v_max=100):
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super().__init__()
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self.env = env
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self.n_atoms = n_atoms
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self.register_buffer("atoms", torch.linspace(v_min, v_max, steps=n_atoms))
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self.n = env.single_action_space.n
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self.network = nn.Sequential(
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nn.Linear(np.array(env.single_observation_space.shape).prod(), 120),
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nn.ReLU(),
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nn.Linear(120, 84),
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nn.ReLU(),
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nn.Linear(84, self.n * n_atoms),
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)
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def get_action(self, x, action=None):
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logits = self.network(x)
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# probability mass function for each action
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pmfs = torch.softmax(logits.view(len(x), self.n, self.n_atoms), dim=2)
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q_values = (pmfs * self.atoms).sum(2)
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if action is None:
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action = torch.argmax(q_values, 1)
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return action, pmfs[torch.arange(len(x)), action]
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def linear_schedule(start_e: float, end_e: float, duration: int, t: int):
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slope = (end_e - start_e) / duration
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return max(slope * t + start_e, end_e)
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123 |
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124 |
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125 |
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if __name__ == "__main__":
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args = parse_args()
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127 |
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run_name = f"{args.env_id}__{args.exp_name}__{args.seed}__{int(time.time())}"
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128 |
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if args.track:
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129 |
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import wandb
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130 |
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wandb.init(
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project=args.wandb_project_name,
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entity=args.wandb_entity,
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sync_tensorboard=True,
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config=vars(args),
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name=run_name,
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monitor_gym=True,
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save_code=True,
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)
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140 |
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writer = SummaryWriter(f"runs/{run_name}")
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141 |
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writer.add_text(
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"hyperparameters",
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"|param|value|\n|-|-|\n%s" % ("\n".join([f"|{key}|{value}|" for key, value in vars(args).items()])),
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)
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145 |
+
|
146 |
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# TRY NOT TO MODIFY: seeding
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147 |
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random.seed(args.seed)
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148 |
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np.random.seed(args.seed)
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149 |
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torch.manual_seed(args.seed)
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150 |
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torch.backends.cudnn.deterministic = args.torch_deterministic
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151 |
+
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152 |
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device = torch.device("cuda" if torch.cuda.is_available() and args.cuda else "cpu")
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153 |
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154 |
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# env setup
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155 |
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envs = gym.vector.SyncVectorEnv([make_env(args.env_id, args.seed, 0, args.capture_video, run_name)])
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156 |
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assert isinstance(envs.single_action_space, gym.spaces.Discrete), "only discrete action space is supported"
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157 |
+
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158 |
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q_network = QNetwork(envs, n_atoms=args.n_atoms, v_min=args.v_min, v_max=args.v_max).to(device)
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159 |
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optimizer = optim.Adam(q_network.parameters(), lr=args.learning_rate, eps=0.01 / args.batch_size)
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160 |
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target_network = QNetwork(envs, n_atoms=args.n_atoms, v_min=args.v_min, v_max=args.v_max).to(device)
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161 |
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target_network.load_state_dict(q_network.state_dict())
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162 |
+
|
163 |
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rb = ReplayBuffer(
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args.buffer_size,
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165 |
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envs.single_observation_space,
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+
envs.single_action_space,
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167 |
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device,
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168 |
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handle_timeout_termination=True,
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169 |
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)
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170 |
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start_time = time.time()
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171 |
+
|
172 |
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# TRY NOT TO MODIFY: start the game
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173 |
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obs = envs.reset()
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174 |
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for global_step in range(args.total_timesteps):
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175 |
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# ALGO LOGIC: put action logic here
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176 |
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epsilon = linear_schedule(args.start_e, args.end_e, args.exploration_fraction * args.total_timesteps, global_step)
|
177 |
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if random.random() < epsilon:
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178 |
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actions = np.array([envs.single_action_space.sample() for _ in range(envs.num_envs)])
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179 |
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else:
|
180 |
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actions, pmf = q_network.get_action(torch.Tensor(obs).to(device))
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181 |
+
actions = actions.cpu().numpy()
|
182 |
+
|
183 |
+
# TRY NOT TO MODIFY: execute the game and log data.
|
184 |
+
next_obs, rewards, dones, infos = envs.step(actions)
|
185 |
+
|
186 |
+
# TRY NOT TO MODIFY: record rewards for plotting purposes
|
187 |
+
for info in infos:
|
188 |
+
if "episode" in info.keys():
|
189 |
+
print(f"global_step={global_step}, episodic_return={info['episode']['r']}")
|
190 |
+
writer.add_scalar("charts/episodic_return", info["episode"]["r"], global_step)
|
191 |
+
writer.add_scalar("charts/episodic_length", info["episode"]["l"], global_step)
|
192 |
+
writer.add_scalar("charts/epsilon", epsilon, global_step)
|
193 |
+
break
|
194 |
+
|
195 |
+
# TRY NOT TO MODIFY: save data to reply buffer; handle `terminal_observation`
|
196 |
+
real_next_obs = next_obs.copy()
|
197 |
+
for idx, d in enumerate(dones):
|
198 |
+
if d:
|
199 |
+
real_next_obs[idx] = infos[idx]["terminal_observation"]
|
200 |
+
rb.add(obs, real_next_obs, actions, rewards, dones, infos)
|
201 |
+
|
202 |
+
# TRY NOT TO MODIFY: CRUCIAL step easy to overlook
|
203 |
+
obs = next_obs
|
204 |
+
|
205 |
+
# ALGO LOGIC: training.
|
206 |
+
if global_step > args.learning_starts:
|
207 |
+
if global_step % args.train_frequency == 0:
|
208 |
+
data = rb.sample(args.batch_size)
|
209 |
+
with torch.no_grad():
|
210 |
+
_, next_pmfs = target_network.get_action(data.next_observations)
|
211 |
+
next_atoms = data.rewards + args.gamma * target_network.atoms * (1 - data.dones)
|
212 |
+
# projection
|
213 |
+
delta_z = target_network.atoms[1] - target_network.atoms[0]
|
214 |
+
tz = next_atoms.clamp(args.v_min, args.v_max)
|
215 |
+
|
216 |
+
b = (tz - args.v_min) / delta_z
|
217 |
+
l = b.floor().clamp(0, args.n_atoms - 1)
|
218 |
+
u = b.ceil().clamp(0, args.n_atoms - 1)
|
219 |
+
# (l == u).float() handles the case where bj is exactly an integer
|
220 |
+
# example bj = 1, then the upper ceiling should be uj= 2, and lj= 1
|
221 |
+
d_m_l = (u + (l == u).float() - b) * next_pmfs
|
222 |
+
d_m_u = (b - l) * next_pmfs
|
223 |
+
target_pmfs = torch.zeros_like(next_pmfs)
|
224 |
+
for i in range(target_pmfs.size(0)):
|
225 |
+
target_pmfs[i].index_add_(0, l[i].long(), d_m_l[i])
|
226 |
+
target_pmfs[i].index_add_(0, u[i].long(), d_m_u[i])
|
227 |
+
|
228 |
+
_, old_pmfs = q_network.get_action(data.observations, data.actions.flatten())
|
229 |
+
loss = (-(target_pmfs * old_pmfs.clamp(min=1e-5, max=1 - 1e-5).log()).sum(-1)).mean()
|
230 |
+
|
231 |
+
if global_step % 100 == 0:
|
232 |
+
writer.add_scalar("losses/loss", loss.item(), global_step)
|
233 |
+
old_val = (old_pmfs * q_network.atoms).sum(1)
|
234 |
+
writer.add_scalar("losses/q_values", old_val.mean().item(), global_step)
|
235 |
+
print("SPS:", int(global_step / (time.time() - start_time)))
|
236 |
+
writer.add_scalar("charts/SPS", int(global_step / (time.time() - start_time)), global_step)
|
237 |
+
|
238 |
+
# optimize the model
|
239 |
+
optimizer.zero_grad()
|
240 |
+
loss.backward()
|
241 |
+
optimizer.step()
|
242 |
+
|
243 |
+
# update the target network
|
244 |
+
if global_step % args.target_network_frequency == 0:
|
245 |
+
target_network.load_state_dict(q_network.state_dict())
|
246 |
+
|
247 |
+
if args.save_model:
|
248 |
+
model_path = f"runs/{run_name}/{args.exp_name}.cleanrl_model"
|
249 |
+
model_data = {
|
250 |
+
"model_weights":q_network.state_dict(),
|
251 |
+
"args":vars(args),
|
252 |
+
}
|
253 |
+
torch.save(model_data, model_path)
|
254 |
+
print(f"model saved to {model_path}")
|
255 |
+
from cleanrl_utils.evals.c51_eval import evaluate
|
256 |
+
|
257 |
+
episodic_returns = evaluate(
|
258 |
+
model_path,
|
259 |
+
make_env,
|
260 |
+
args.env_id,
|
261 |
+
eval_episodes=10,
|
262 |
+
run_name=f"{run_name}-eval",
|
263 |
+
Model=QNetwork,
|
264 |
+
device=device,
|
265 |
+
epsilon=0.05,
|
266 |
+
)
|
267 |
+
for idx, episodic_return in enumerate(episodic_returns):
|
268 |
+
writer.add_scalar("eval/episodic_return", episodic_return, idx)
|
269 |
+
|
270 |
+
if args.upload_model:
|
271 |
+
from cleanrl_utils.huggingface import push_to_hub
|
272 |
+
|
273 |
+
repo_name = f"{args.env_id}-{args.exp_name}-seed{args.seed}"
|
274 |
+
repo_id = f"{args.hf_entity}/{repo_name}" if args.hf_entity else repo_name
|
275 |
+
push_to_hub(args, episodic_returns, repo_id, "C51", f"runs/{run_name}", f"videos/{run_name}-eval")
|
276 |
+
|
277 |
+
envs.close()
|
278 |
+
writer.close()
|
events.out.tfevents.1672465429.fedora.998969.0
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:3b7a49809eba1fa1080c8eca9f68c571226bbc1b02ada325455fb341e5723073
|
3 |
+
size 1564048
|
poetry.lock
ADDED
The diff for this file is too large to render.
See raw diff
|
|
pyproject.toml
ADDED
@@ -0,0 +1,158 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
[tool.poetry]
|
2 |
+
name = "cleanrl-test"
|
3 |
+
version = "1.1.0"
|
4 |
+
description = "High-quality single file implementation of Deep Reinforcement Learning algorithms with research-friendly features"
|
5 |
+
authors = ["Costa Huang <costa.huang@outlook.com>"]
|
6 |
+
packages = [
|
7 |
+
{ include = "cleanrl" },
|
8 |
+
{ include = "cleanrl_utils" },
|
9 |
+
]
|
10 |
+
keywords = ["reinforcement", "machine", "learning", "research"]
|
11 |
+
license="MIT"
|
12 |
+
readme = "README.md"
|
13 |
+
|
14 |
+
[tool.poetry.dependencies]
|
15 |
+
python = ">=3.7.1,<3.10"
|
16 |
+
tensorboard = "^2.10.0"
|
17 |
+
wandb = "^0.13.6"
|
18 |
+
gym = "0.23.1"
|
19 |
+
torch = ">=1.12.1"
|
20 |
+
stable-baselines3 = "1.2.0"
|
21 |
+
gymnasium = "^0.26.3"
|
22 |
+
moviepy = "^1.0.3"
|
23 |
+
pygame = "2.1.0"
|
24 |
+
huggingface-hub = "^0.11.1"
|
25 |
+
|
26 |
+
ale-py = {version = "0.7.4", optional = true}
|
27 |
+
AutoROM = {extras = ["accept-rom-license"], version = "^0.4.2"}
|
28 |
+
opencv-python = {version = "^4.6.0.66", optional = true}
|
29 |
+
pybullet = {version = "3.1.8", optional = true}
|
30 |
+
procgen = {version = "^0.10.7", optional = true}
|
31 |
+
pytest = {version = "^7.1.3", optional = true}
|
32 |
+
mujoco = {version = "^2.2", optional = true}
|
33 |
+
imageio = {version = "^2.14.1", optional = true}
|
34 |
+
free-mujoco-py = {version = "^2.1.6", optional = true}
|
35 |
+
mkdocs-material = {version = "^8.4.3", optional = true}
|
36 |
+
markdown-include = {version = "^0.7.0", optional = true}
|
37 |
+
jax = {version = "^0.3.17", optional = true}
|
38 |
+
jaxlib = {version = "^0.3.15", optional = true}
|
39 |
+
flax = {version = "^0.6.0", optional = true}
|
40 |
+
optuna = {version = "^3.0.1", optional = true}
|
41 |
+
optuna-dashboard = {version = "^0.7.2", optional = true}
|
42 |
+
rich = {version = "<12.0", optional = true}
|
43 |
+
envpool = {version = "^0.6.4", optional = true}
|
44 |
+
PettingZoo = {version = "1.18.1", optional = true}
|
45 |
+
SuperSuit = {version = "3.4.0", optional = true}
|
46 |
+
multi-agent-ale-py = {version = "0.1.11", optional = true}
|
47 |
+
boto3 = {version = "^1.24.70", optional = true}
|
48 |
+
awscli = {version = "^1.25.71", optional = true}
|
49 |
+
shimmy = {version = "^0.1.0", optional = true}
|
50 |
+
dm-control = {version = "^1.0.8", optional = true}
|
51 |
+
|
52 |
+
[tool.poetry.group.dev.dependencies]
|
53 |
+
pre-commit = "^2.20.0"
|
54 |
+
|
55 |
+
[tool.poetry.group.atari]
|
56 |
+
optional = true
|
57 |
+
[tool.poetry.group.atari.dependencies]
|
58 |
+
ale-py = "0.7.4"
|
59 |
+
AutoROM = {extras = ["accept-rom-license"], version = "^0.4.2"}
|
60 |
+
opencv-python = "^4.6.0.66"
|
61 |
+
|
62 |
+
[tool.poetry.group.pybullet]
|
63 |
+
optional = true
|
64 |
+
[tool.poetry.group.pybullet.dependencies]
|
65 |
+
pybullet = "3.1.8"
|
66 |
+
|
67 |
+
[tool.poetry.group.procgen]
|
68 |
+
optional = true
|
69 |
+
[tool.poetry.group.procgen.dependencies]
|
70 |
+
procgen = "^0.10.7"
|
71 |
+
|
72 |
+
[tool.poetry.group.pytest]
|
73 |
+
optional = true
|
74 |
+
[tool.poetry.group.pytest.dependencies]
|
75 |
+
pytest = "^7.1.3"
|
76 |
+
|
77 |
+
[tool.poetry.group.mujoco]
|
78 |
+
optional = true
|
79 |
+
[tool.poetry.group.mujoco.dependencies]
|
80 |
+
mujoco = "^2.2"
|
81 |
+
imageio = "^2.14.1"
|
82 |
+
|
83 |
+
[tool.poetry.group.mujoco_py]
|
84 |
+
optional = true
|
85 |
+
[tool.poetry.group.mujoco_py.dependencies]
|
86 |
+
free-mujoco-py = "^2.1.6"
|
87 |
+
|
88 |
+
[tool.poetry.group.docs]
|
89 |
+
optional = true
|
90 |
+
[tool.poetry.group.docs.dependencies]
|
91 |
+
mkdocs-material = "^8.4.3"
|
92 |
+
markdown-include = "^0.7.0"
|
93 |
+
|
94 |
+
[tool.poetry.group.jax]
|
95 |
+
optional = true
|
96 |
+
[tool.poetry.group.jax.dependencies]
|
97 |
+
jax = "^0.3.17"
|
98 |
+
jaxlib = "^0.3.15"
|
99 |
+
flax = "^0.6.0"
|
100 |
+
|
101 |
+
[tool.poetry.group.optuna]
|
102 |
+
optional = true
|
103 |
+
[tool.poetry.group.optuna.dependencies]
|
104 |
+
optuna = "^3.0.1"
|
105 |
+
optuna-dashboard = "^0.7.2"
|
106 |
+
rich = "<12.0"
|
107 |
+
|
108 |
+
[tool.poetry.group.envpool]
|
109 |
+
optional = true
|
110 |
+
[tool.poetry.group.envpool.dependencies]
|
111 |
+
envpool = "^0.6.4"
|
112 |
+
|
113 |
+
[tool.poetry.group.pettingzoo]
|
114 |
+
optional = true
|
115 |
+
[tool.poetry.group.pettingzoo.dependencies]
|
116 |
+
PettingZoo = "1.18.1"
|
117 |
+
SuperSuit = "3.4.0"
|
118 |
+
multi-agent-ale-py = "0.1.11"
|
119 |
+
|
120 |
+
[tool.poetry.group.cloud]
|
121 |
+
optional = true
|
122 |
+
[tool.poetry.group.cloud.dependencies]
|
123 |
+
boto3 = "^1.24.70"
|
124 |
+
awscli = "^1.25.71"
|
125 |
+
|
126 |
+
[tool.poetry.group.isaacgym]
|
127 |
+
optional = true
|
128 |
+
[tool.poetry.group.isaacgym.dependencies]
|
129 |
+
isaacgymenvs = {git = "https://github.com/vwxyzjn/IsaacGymEnvs.git", rev = "poetry"}
|
130 |
+
isaacgym = {path = "cleanrl/ppo_continuous_action_isaacgym/isaacgym", develop = true}
|
131 |
+
|
132 |
+
[tool.poetry.group.dm_control]
|
133 |
+
optional = true
|
134 |
+
[tool.poetry.group.dm_control.dependencies]
|
135 |
+
shimmy = "^0.1.0"
|
136 |
+
dm-control = "^1.0.8"
|
137 |
+
mujoco = "^2.2"
|
138 |
+
|
139 |
+
[build-system]
|
140 |
+
requires = ["poetry-core"]
|
141 |
+
build-backend = "poetry.core.masonry.api"
|
142 |
+
|
143 |
+
[tool.poetry.extras]
|
144 |
+
atari = ["ale-py", "AutoROM", "opencv-python"]
|
145 |
+
pybullet = ["pybullet"]
|
146 |
+
procgen = ["procgen"]
|
147 |
+
plot = ["pandas", "seaborn"]
|
148 |
+
spyder = ["spyder"]
|
149 |
+
pytest = ["pytest"]
|
150 |
+
mujoco = ["mujoco", "imageio"]
|
151 |
+
mujoco_py = ["free-mujoco-py"]
|
152 |
+
jax = ["jax", "jaxlib", "flax"]
|
153 |
+
docs = ["mkdocs-material", "markdown-include"]
|
154 |
+
envpool = ["envpool"]
|
155 |
+
optuna = ["optuna", "optuna-dashboard", "rich"]
|
156 |
+
pettingzoo = ["PettingZoo", "SuperSuit", "multi-agent-ale-py"]
|
157 |
+
cloud = ["boto3", "awscli"]
|
158 |
+
dm_control = ["shimmy", "dm-control", "mujoco"]
|
replay.mp4
ADDED
Binary file (40.2 kB). View file
|
|
videos/CartPole-v1__c51__1__1672465429-eval/rl-video-episode-0.mp4
ADDED
Binary file (42.9 kB). View file
|
|
videos/CartPole-v1__c51__1__1672465429-eval/rl-video-episode-1.mp4
ADDED
Binary file (41.9 kB). View file
|
|
videos/CartPole-v1__c51__1__1672465429-eval/rl-video-episode-8.mp4
ADDED
Binary file (40.2 kB). View file
|
|