kinalmehta commited on
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pushing model

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README.md ADDED
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1
+ ---
2
+ tags:
3
+ - MountainCar-v0
4
+ - deep-reinforcement-learning
5
+ - reinforcement-learning
6
+ - custom-implementation
7
+ library_name: cleanrl
8
+ model-index:
9
+ - name: DQN
10
+ results:
11
+ - task:
12
+ type: reinforcement-learning
13
+ name: reinforcement-learning
14
+ dataset:
15
+ name: MountainCar-v0
16
+ type: MountainCar-v0
17
+ metrics:
18
+ - type: mean_reward
19
+ value: -200.00 +/- 0.00
20
+ name: mean_reward
21
+ verified: false
22
+ ---
23
+
24
+ # (CleanRL) **DQN** Agent Playing **MountainCar-v0**
25
+
26
+ This is a trained model of a DQN agent playing MountainCar-v0.
27
+ The model was trained by using [CleanRL](https://github.com/vwxyzjn/cleanrl) and the most up-to-date training code can be
28
+ found [here](https://github.com/vwxyzjn/cleanrl/blob/master/cleanrl/c51_jax.py).
29
+
30
+ ## Get Started
31
+
32
+ To use this model, please install the `cleanrl` package with the following command:
33
+
34
+ ```
35
+ pip install "cleanrl[c51_jax]"
36
+ python -m cleanrl_utils.enjoy --exp-name c51_jax --env-id MountainCar-v0
37
+ ```
38
+
39
+ Please refer to the [documentation](https://docs.cleanrl.dev/get-started/zoo/) for more detail.
40
+
41
+
42
+ ## Command to reproduce the training
43
+
44
+ ```bash
45
+ curl -OL https://huggingface.co/cleanrl/MountainCar-v0-c51_jax-seed1/raw/main/c51_jax.py
46
+ curl -OL https://huggingface.co/cleanrl/MountainCar-v0-c51_jax-seed1/raw/main/pyproject.toml
47
+ curl -OL https://huggingface.co/cleanrl/MountainCar-v0-c51_jax-seed1/raw/main/poetry.lock
48
+ poetry install --all-extras
49
+ python c51_jax.py --save-model --upload-model --hf-entity cleanrl --env-id MountainCar-v0
50
+ ```
51
+
52
+ # Hyperparameters
53
+ ```python
54
+ {'batch_size': 128,
55
+ 'buffer_size': 10000,
56
+ 'capture_video': False,
57
+ 'end_e': 0.05,
58
+ 'env_id': 'MountainCar-v0',
59
+ 'exp_name': 'c51_jax',
60
+ 'exploration_fraction': 0.5,
61
+ 'gamma': 0.99,
62
+ 'hf_entity': 'cleanrl',
63
+ 'learning_rate': 0.00025,
64
+ 'learning_starts': 10000,
65
+ 'n_atoms': 101,
66
+ 'save_model': True,
67
+ 'seed': 1,
68
+ 'start_e': 1,
69
+ 'target_network_frequency': 500,
70
+ 'total_timesteps': 500000,
71
+ 'track': False,
72
+ 'train_frequency': 10,
73
+ 'upload_model': True,
74
+ 'v_max': 100,
75
+ 'v_min': -100,
76
+ 'wandb_entity': None,
77
+ 'wandb_project_name': 'cleanRL'}
78
+ ```
79
+
c51_jax.cleanrl_model ADDED
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c51_jax.py ADDED
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1
+ # docs and experiment results can be found at https://docs.cleanrl.dev/rl-algorithms/c51/#c51_jaxpy
2
+ import argparse
3
+ import os
4
+ import random
5
+ import time
6
+ from distutils.util import strtobool
7
+
8
+ import flax
9
+ import flax.linen as nn
10
+ import gym
11
+ import jax
12
+ import jax.numpy as jnp
13
+ import numpy as np
14
+ import optax
15
+ from flax.training.train_state import TrainState
16
+ from stable_baselines3.common.buffers import ReplayBuffer
17
+ from torch.utils.tensorboard import SummaryWriter
18
+
19
+
20
+ def parse_args():
21
+ # fmt: off
22
+ parser = argparse.ArgumentParser()
23
+ parser.add_argument("--exp-name", type=str, default=os.path.basename(__file__).rstrip(".py"),
24
+ help="the name of this experiment")
25
+ parser.add_argument("--seed", type=int, default=1,
26
+ help="seed of the experiment")
27
+ parser.add_argument("--track", type=lambda x: bool(strtobool(x)), default=False, nargs="?", const=True,
28
+ help="if toggled, this experiment will be tracked with Weights and Biases")
29
+ parser.add_argument("--wandb-project-name", type=str, default="cleanRL",
30
+ help="the wandb's project name")
31
+ parser.add_argument("--wandb-entity", type=str, default=None,
32
+ help="the entity (team) of wandb's project")
33
+ parser.add_argument("--capture-video", type=lambda x: bool(strtobool(x)), default=False, nargs="?", const=True,
34
+ help="whether to capture videos of the agent performances (check out `videos` folder)")
35
+ parser.add_argument("--save-model", type=lambda x: bool(strtobool(x)), default=False, nargs="?", const=True,
36
+ help="whether to save model into the `runs/{run_name}` folder")
37
+ parser.add_argument("--upload-model", type=lambda x: bool(strtobool(x)), default=False, nargs="?", const=True,
38
+ help="whether to upload the saved model to huggingface")
39
+ parser.add_argument("--hf-entity", type=str, default="",
40
+ help="the user or org name of the model repository from the Hugging Face Hub")
41
+
42
+ # Algorithm specific arguments
43
+ parser.add_argument("--env-id", type=str, default="CartPole-v1",
44
+ help="the id of the environment")
45
+ parser.add_argument("--total-timesteps", type=int, default=500000,
46
+ help="total timesteps of the experiments")
47
+ parser.add_argument("--learning-rate", type=float, default=2.5e-4,
48
+ help="the learning rate of the optimizer")
49
+ parser.add_argument("--n-atoms", type=int, default=101,
50
+ help="the number of atoms")
51
+ parser.add_argument("--v-min", type=float, default=-100,
52
+ help="the number of atoms")
53
+ parser.add_argument("--v-max", type=float, default=100,
54
+ help="the number of atoms")
55
+ parser.add_argument("--buffer-size", type=int, default=10000,
56
+ help="the replay memory buffer size")
57
+ parser.add_argument("--gamma", type=float, default=0.99,
58
+ help="the discount factor gamma")
59
+ parser.add_argument("--target-network-frequency", type=int, default=500,
60
+ help="the timesteps it takes to update the target network")
61
+ parser.add_argument("--batch-size", type=int, default=128,
62
+ help="the batch size of sample from the reply memory")
63
+ parser.add_argument("--start-e", type=float, default=1,
64
+ help="the starting epsilon for exploration")
65
+ parser.add_argument("--end-e", type=float, default=0.05,
66
+ help="the ending epsilon for exploration")
67
+ parser.add_argument("--exploration-fraction", type=float, default=0.5,
68
+ help="the fraction of `total-timesteps` it takes from start-e to go end-e")
69
+ parser.add_argument("--learning-starts", type=int, default=10000,
70
+ help="timestep to start learning")
71
+ parser.add_argument("--train-frequency", type=int, default=10,
72
+ help="the frequency of training")
73
+ args = parser.parse_args()
74
+ # fmt: on
75
+ return args
76
+
77
+
78
+ def make_env(env_id, seed, idx, capture_video, run_name):
79
+ def thunk():
80
+ env = gym.make(env_id)
81
+ env = gym.wrappers.RecordEpisodeStatistics(env)
82
+ if capture_video:
83
+ if idx == 0:
84
+ env = gym.wrappers.RecordVideo(env, f"videos/{run_name}")
85
+ env.seed(seed)
86
+ env.action_space.seed(seed)
87
+ env.observation_space.seed(seed)
88
+ return env
89
+
90
+ return thunk
91
+
92
+
93
+ # ALGO LOGIC: initialize agent here:
94
+ class QNetwork(nn.Module):
95
+ action_dim: int
96
+ n_atoms: int
97
+
98
+ @nn.compact
99
+ def __call__(self, x):
100
+ x = nn.Dense(120)(x)
101
+ x = nn.relu(x)
102
+ x = nn.Dense(84)(x)
103
+ x = nn.relu(x)
104
+ x = nn.Dense(self.action_dim * self.n_atoms)(x)
105
+ x = x.reshape((x.shape[0], self.action_dim, self.n_atoms))
106
+ x = nn.softmax(x, axis=-1) # pmfs
107
+ return x
108
+
109
+
110
+ class TrainState(TrainState):
111
+ target_params: flax.core.FrozenDict
112
+ atoms: jnp.ndarray
113
+
114
+
115
+ def linear_schedule(start_e: float, end_e: float, duration: int, t: int):
116
+ slope = (end_e - start_e) / duration
117
+ return max(slope * t + start_e, end_e)
118
+
119
+
120
+ if __name__ == "__main__":
121
+ args = parse_args()
122
+ run_name = f"{args.env_id}__{args.exp_name}__{args.seed}__{int(time.time())}"
123
+ if args.track:
124
+ import wandb
125
+
126
+ wandb.init(
127
+ project=args.wandb_project_name,
128
+ entity=args.wandb_entity,
129
+ sync_tensorboard=True,
130
+ config=vars(args),
131
+ name=run_name,
132
+ monitor_gym=True,
133
+ save_code=True,
134
+ )
135
+ writer = SummaryWriter(f"runs/{run_name}")
136
+ writer.add_text(
137
+ "hyperparameters",
138
+ "|param|value|\n|-|-|\n%s" % ("\n".join([f"|{key}|{value}|" for key, value in vars(args).items()])),
139
+ )
140
+
141
+ # TRY NOT TO MODIFY: seeding
142
+ random.seed(args.seed)
143
+ np.random.seed(args.seed)
144
+ key = jax.random.PRNGKey(args.seed)
145
+ key, q_key = jax.random.split(key, 2)
146
+
147
+ # env setup
148
+ envs = gym.vector.SyncVectorEnv([make_env(args.env_id, args.seed, 0, args.capture_video, run_name)])
149
+ assert isinstance(envs.single_action_space, gym.spaces.Discrete), "only discrete action space is supported"
150
+
151
+ obs = envs.reset()
152
+ q_network = QNetwork(action_dim=envs.single_action_space.n, n_atoms=args.n_atoms)
153
+ q_state = TrainState.create(
154
+ apply_fn=q_network.apply,
155
+ params=q_network.init(q_key, obs),
156
+ target_params=q_network.init(q_key, obs),
157
+ # directly using jnp.linspace leads to numerical errors
158
+ atoms=jnp.asarray(np.linspace(args.v_min, args.v_max, num=args.n_atoms)),
159
+ tx=optax.adam(learning_rate=args.learning_rate, eps=0.01 / args.batch_size),
160
+ )
161
+ q_network.apply = jax.jit(q_network.apply)
162
+ # This step is not necessary as init called on same observation and key will always lead to same initializations
163
+ q_state = q_state.replace(target_params=optax.incremental_update(q_state.params, q_state.target_params, 1))
164
+
165
+ rb = ReplayBuffer(
166
+ args.buffer_size,
167
+ envs.single_observation_space,
168
+ envs.single_action_space,
169
+ "cpu",
170
+ handle_timeout_termination=True,
171
+ )
172
+
173
+ @jax.jit
174
+ def update(q_state, observations, actions, next_observations, rewards, dones):
175
+ next_pmfs = q_network.apply(q_state.target_params, next_observations) # (batch_size, num_actions, num_atoms)
176
+ next_vals = (next_pmfs * q_state.atoms).sum(axis=-1) # (batch_size, num_actions)
177
+ next_action = jnp.argmax(next_vals, axis=-1) # (batch_size,)
178
+ next_pmfs = next_pmfs[np.arange(next_pmfs.shape[0]), next_action]
179
+ next_atoms = rewards + args.gamma * q_state.atoms * (1 - dones)
180
+ # projection
181
+ delta_z = q_state.atoms[1] - q_state.atoms[0]
182
+ tz = jnp.clip(next_atoms, a_min=(args.v_min), a_max=(args.v_max))
183
+
184
+ b = (tz - args.v_min) / delta_z
185
+ l = jnp.clip(jnp.floor(b), a_min=0, a_max=args.n_atoms - 1)
186
+ u = jnp.clip(jnp.ceil(b), a_min=0, a_max=args.n_atoms - 1)
187
+ # (l == u).astype(jnp.float) handles the case where bj is exactly an integer
188
+ # example bj = 1, then the upper ceiling should be uj= 2, and lj= 1
189
+ d_m_l = (u + (l == u).astype(jnp.float32) - b) * next_pmfs
190
+ d_m_u = (b - l) * next_pmfs
191
+ target_pmfs = jnp.zeros_like(next_pmfs)
192
+
193
+ def project_to_bins(i, val):
194
+ val = val.at[i, l[i].astype(jnp.int32)].add(d_m_l[i])
195
+ val = val.at[i, u[i].astype(jnp.int32)].add(d_m_u[i])
196
+ return val
197
+
198
+ target_pmfs = jax.lax.fori_loop(0, target_pmfs.shape[0], project_to_bins, target_pmfs)
199
+
200
+ def loss(q_params, observations, actions, target_pmfs):
201
+ pmfs = q_network.apply(q_params, observations)
202
+ old_pmfs = pmfs[np.arange(pmfs.shape[0]), actions.squeeze()]
203
+
204
+ old_pmfs_l = jnp.clip(old_pmfs, a_min=1e-5, a_max=1 - 1e-5)
205
+ loss = (-(target_pmfs * jnp.log(old_pmfs_l)).sum(-1)).mean()
206
+ return loss, (old_pmfs * q_state.atoms).sum(-1)
207
+
208
+ (loss_value, old_values), grads = jax.value_and_grad(loss, has_aux=True)(
209
+ q_state.params, observations, actions, target_pmfs
210
+ )
211
+ q_state = q_state.apply_gradients(grads=grads)
212
+ return loss_value, old_values, q_state
213
+
214
+ start_time = time.time()
215
+
216
+ # TRY NOT TO MODIFY: start the game
217
+ obs = envs.reset()
218
+ for global_step in range(args.total_timesteps):
219
+ # ALGO LOGIC: put action logic here
220
+ epsilon = linear_schedule(args.start_e, args.end_e, args.exploration_fraction * args.total_timesteps, global_step)
221
+ if random.random() < epsilon:
222
+ actions = np.array([envs.single_action_space.sample() for _ in range(envs.num_envs)])
223
+ else:
224
+ pmfs = q_network.apply(q_state.params, obs)
225
+ q_vals = (pmfs * q_state.atoms).sum(axis=-1)
226
+ actions = q_vals.argmax(axis=-1)
227
+ actions = jax.device_get(actions)
228
+
229
+ # TRY NOT TO MODIFY: execute the game and log data.
230
+ next_obs, rewards, dones, infos = envs.step(actions)
231
+
232
+ # TRY NOT TO MODIFY: record rewards for plotting purposes
233
+ for info in infos:
234
+ if "episode" in info.keys():
235
+ print(f"global_step={global_step}, episodic_return={info['episode']['r']}")
236
+ writer.add_scalar("charts/episodic_return", info["episode"]["r"], global_step)
237
+ writer.add_scalar("charts/episodic_length", info["episode"]["l"], global_step)
238
+ writer.add_scalar("charts/epsilon", epsilon, global_step)
239
+ break
240
+
241
+ # TRY NOT TO MODIFY: save data to reply buffer; handle `terminal_observation`
242
+ real_next_obs = next_obs.copy()
243
+ for idx, d in enumerate(dones):
244
+ if d:
245
+ real_next_obs[idx] = infos[idx]["terminal_observation"]
246
+ rb.add(obs, real_next_obs, actions, rewards, dones, infos)
247
+
248
+ # TRY NOT TO MODIFY: CRUCIAL step easy to overlook
249
+ obs = next_obs
250
+
251
+ # ALGO LOGIC: training.
252
+ if global_step > args.learning_starts and global_step % args.train_frequency == 0:
253
+ data = rb.sample(args.batch_size)
254
+ loss, old_val, q_state = update(
255
+ q_state,
256
+ data.observations.numpy(),
257
+ data.actions.numpy(),
258
+ data.next_observations.numpy(),
259
+ data.rewards.numpy(),
260
+ data.dones.numpy(),
261
+ )
262
+
263
+ if global_step % 100 == 0:
264
+ writer.add_scalar("losses/loss", jax.device_get(loss), global_step)
265
+ writer.add_scalar("losses/q_values", jax.device_get(old_val.mean()), global_step)
266
+ print("SPS:", int(global_step / (time.time() - start_time)))
267
+ writer.add_scalar("charts/SPS", int(global_step / (time.time() - start_time)), global_step)
268
+
269
+ # update the target network
270
+ if global_step % args.target_network_frequency == 0:
271
+ q_state = q_state.replace(target_params=optax.incremental_update(q_state.params, q_state.target_params, 1))
272
+
273
+ if args.save_model:
274
+ model_path = f"runs/{run_name}/{args.exp_name}.cleanrl_model"
275
+ model_data = {
276
+ "model_weights": q_state.params,
277
+ "args": vars(args),
278
+ }
279
+ with open(model_path, "wb") as f:
280
+ f.write(flax.serialization.to_bytes(model_data))
281
+ print(f"model saved to {model_path}")
282
+ from cleanrl_utils.evals.c51_jax_eval import evaluate
283
+
284
+ episodic_returns = evaluate(
285
+ model_path,
286
+ make_env,
287
+ args.env_id,
288
+ eval_episodes=10,
289
+ run_name=f"{run_name}-eval",
290
+ Model=QNetwork,
291
+ epsilon=0.05,
292
+ )
293
+ for idx, episodic_return in enumerate(episodic_returns):
294
+ writer.add_scalar("eval/episodic_return", episodic_return, idx)
295
+
296
+ if args.upload_model:
297
+ from cleanrl_utils.huggingface import push_to_hub
298
+
299
+ repo_name = f"{args.env_id}-{args.exp_name}-seed{args.seed}"
300
+ repo_id = f"{args.hf_entity}/{repo_name}" if args.hf_entity else repo_name
301
+ push_to_hub(args, episodic_returns, repo_id, "DQN", f"runs/{run_name}", f"videos/{run_name}-eval")
302
+
303
+ envs.close()
304
+ writer.close()
events.out.tfevents.1672601060.fedora.38904.0 ADDED
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1
+ version https://git-lfs.github.com/spec/v1
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+ oid sha256:41c447dde582a32335d329b92e4593751ae8b6921e1ec986ca69f65fc5f6ade5
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+ size 1210173
poetry.lock ADDED
The diff for this file is too large to render. See raw diff
 
pyproject.toml ADDED
@@ -0,0 +1,178 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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 <[email protected]>"]
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
+ pytest = ["pytest"]
149
+ mujoco = ["mujoco", "imageio"]
150
+ mujoco_py = ["free-mujoco-py"]
151
+ jax = ["jax", "jaxlib", "flax"]
152
+ docs = ["mkdocs-material", "markdown-include"]
153
+ envpool = ["envpool"]
154
+ optuna = ["optuna", "optuna-dashboard", "rich"]
155
+ pettingzoo = ["PettingZoo", "SuperSuit", "multi-agent-ale-py"]
156
+ cloud = ["boto3", "awscli"]
157
+ dm_control = ["shimmy", "dm-control", "mujoco"]
158
+
159
+ # dependencies for algorithm variant (useful when you want to run a specific algorithm)
160
+ dqn = []
161
+ dqn_atari = ["ale-py", "AutoROM", "opencv-python"]
162
+ dqn_jax = ["jax", "jaxlib", "flax"]
163
+ dqn_atari_jax = [
164
+ "ale-py", "AutoROM", "opencv-python", # atari
165
+ "jax", "jaxlib", "flax" # jax
166
+ ]
167
+ c51 = []
168
+ c51_atari = ["ale-py", "AutoROM", "opencv-python"]
169
+ c51_jax = ["jax", "jaxlib", "flax"]
170
+ c51_atari_jax = [
171
+ "ale-py", "AutoROM", "opencv-python", # atari
172
+ "jax", "jaxlib", "flax" # jax
173
+ ]
174
+ ppo_atari_envpool_xla_jax_scan = [
175
+ "ale-py", "AutoROM", "opencv-python", # atari
176
+ "jax", "jaxlib", "flax", # jax
177
+ "envpool", # envpool
178
+ ]
replay.mp4 ADDED
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videos/MountainCar-v0__c51_jax__1__1672601060-eval/rl-video-episode-0.mp4 ADDED
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videos/MountainCar-v0__c51_jax__1__1672601060-eval/rl-video-episode-1.mp4 ADDED
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videos/MountainCar-v0__c51_jax__1__1672601060-eval/rl-video-episode-8.mp4 ADDED
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