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c51_jax.py
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c51_jax.py
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# docs and experiment results can be found at https://docs.cleanrl.dev/rl-algorithms/c51/#c51_jaxpy
import os
import random
import time
from dataclasses import dataclass
import flax
import flax.linen as nn
import gymnasium as gym
import jax
import jax.numpy as jnp
import numpy as np
import optax
import tyro
from flax.training.train_state import TrainState
from stable_baselines3.common.buffers import ReplayBuffer
from torch.utils.tensorboard import SummaryWriter
@dataclass
class Args:
exp_name: str = os.path.basename(__file__)[: -len(".py")]
"""the name of this experiment"""
seed: int = 1
"""seed of the experiment"""
track: bool = False
"""if toggled, this experiment will be tracked with Weights and Biases"""
wandb_project_name: str = "cleanRL"
"""the wandb's project name"""
wandb_entity: str = None
"""the entity (team) of wandb's project"""
capture_video: bool = False
"""whether to capture videos of the agent performances (check out `videos` folder)"""
save_model: bool = False
"""whether to save model into the `runs/{run_name}` folder"""
upload_model: bool = False
"""whether to upload the saved model to huggingface"""
hf_entity: str = ""
"""the user or org name of the model repository from the Hugging Face Hub"""
# Algorithm specific arguments
env_id: str = "CartPole-v1"
"""the id of the environment"""
total_timesteps: int = 500000
"""total timesteps of the experiments"""
learning_rate: float = 2.5e-4
"""the learning rate of the optimizer"""
num_envs: int = 1
"""the number of parallel game environments"""
n_atoms: int = 101
"""the number of atoms"""
v_min: float = -100
"""the return lower bound"""
v_max: float = 100
"""the return upper bound"""
buffer_size: int = 10000
"""the replay memory buffer size"""
gamma: float = 0.99
"""the discount factor gamma"""
target_network_frequency: int = 500
"""the timesteps it takes to update the target network"""
batch_size: int = 128
"""the batch size of sample from the reply memory"""
start_e: float = 1
"""the starting epsilon for exploration"""
end_e: float = 0.05
"""the ending epsilon for exploration"""
exploration_fraction: float = 0.5
"""the fraction of `total-timesteps` it takes from start-e to go end-e"""
learning_starts: int = 10000
"""timestep to start learning"""
train_frequency: int = 10
"""the frequency of training"""
def make_env(env_id, seed, idx, capture_video, run_name):
def thunk():
if capture_video and idx == 0:
env = gym.make(env_id, render_mode="rgb_array")
env = gym.wrappers.RecordVideo(env, f"videos/{run_name}")
else:
env = gym.make(env_id)
env = gym.wrappers.RecordEpisodeStatistics(env)
env.action_space.seed(seed)
return env
return thunk
# ALGO LOGIC: initialize agent here:
class QNetwork(nn.Module):
action_dim: int
n_atoms: int
@nn.compact
def __call__(self, x):
x = nn.Dense(120)(x)
x = nn.relu(x)
x = nn.Dense(84)(x)
x = nn.relu(x)
x = nn.Dense(self.action_dim * self.n_atoms)(x)
x = x.reshape((x.shape[0], self.action_dim, self.n_atoms))
x = nn.softmax(x, axis=-1) # pmfs
return x
class TrainState(TrainState):
target_params: flax.core.FrozenDict
atoms: jnp.ndarray
def linear_schedule(start_e: float, end_e: float, duration: int, t: int):
slope = (end_e - start_e) / duration
return max(slope * t + start_e, end_e)
if __name__ == "__main__":
import stable_baselines3 as sb3
if sb3.__version__ < "2.0":
raise ValueError(
"""Ongoing migration: run the following command to install the new dependencies:
poetry run pip install "stable_baselines3==2.0.0a1"
"""
)
args = tyro.cli(Args)
assert args.num_envs == 1, "vectorized envs are not supported at the moment"
run_name = f"{args.env_id}__{args.exp_name}__{args.seed}__{int(time.time())}"
if args.track:
import wandb
wandb.init(
project=args.wandb_project_name,
entity=args.wandb_entity,
sync_tensorboard=True,
config=vars(args),
name=run_name,
monitor_gym=True,
save_code=True,
)
writer = SummaryWriter(f"runs/{run_name}")
writer.add_text(
"hyperparameters",
"|param|value|\n|-|-|\n%s" % ("\n".join([f"|{key}|{value}|" for key, value in vars(args).items()])),
)
# TRY NOT TO MODIFY: seeding
random.seed(args.seed)
np.random.seed(args.seed)
key = jax.random.PRNGKey(args.seed)
key, q_key = jax.random.split(key, 2)
# env setup
envs = gym.vector.SyncVectorEnv(
[make_env(args.env_id, args.seed + i, i, args.capture_video, run_name) for i in range(args.num_envs)]
)
assert isinstance(envs.single_action_space, gym.spaces.Discrete), "only discrete action space is supported"
obs, _ = envs.reset(seed=args.seed)
q_network = QNetwork(action_dim=envs.single_action_space.n, n_atoms=args.n_atoms)
q_state = TrainState.create(
apply_fn=q_network.apply,
params=q_network.init(q_key, obs),
target_params=q_network.init(q_key, obs),
# directly using jnp.linspace leads to numerical errors
atoms=jnp.asarray(np.linspace(args.v_min, args.v_max, num=args.n_atoms)),
tx=optax.adam(learning_rate=args.learning_rate, eps=0.01 / args.batch_size),
)
q_network.apply = jax.jit(q_network.apply)
# This step is not necessary as init called on same observation and key will always lead to same initializations
q_state = q_state.replace(target_params=optax.incremental_update(q_state.params, q_state.target_params, 1))
rb = ReplayBuffer(
args.buffer_size,
envs.single_observation_space,
envs.single_action_space,
"cpu",
handle_timeout_termination=False,
)
@jax.jit
def update(q_state, observations, actions, next_observations, rewards, dones):
next_pmfs = q_network.apply(q_state.target_params, next_observations) # (batch_size, num_actions, num_atoms)
next_vals = (next_pmfs * q_state.atoms).sum(axis=-1) # (batch_size, num_actions)
next_action = jnp.argmax(next_vals, axis=-1) # (batch_size,)
next_pmfs = next_pmfs[np.arange(next_pmfs.shape[0]), next_action]
next_atoms = rewards + args.gamma * q_state.atoms * (1 - dones)
# projection
delta_z = q_state.atoms[1] - q_state.atoms[0]
tz = jnp.clip(next_atoms, a_min=(args.v_min), a_max=(args.v_max))
b = (tz - args.v_min) / delta_z
l = jnp.clip(jnp.floor(b), a_min=0, a_max=args.n_atoms - 1)
u = jnp.clip(jnp.ceil(b), a_min=0, a_max=args.n_atoms - 1)
# (l == u).astype(jnp.float) handles the case where bj is exactly an integer
# example bj = 1, then the upper ceiling should be uj= 2, and lj= 1
d_m_l = (u + (l == u).astype(jnp.float32) - b) * next_pmfs
d_m_u = (b - l) * next_pmfs
target_pmfs = jnp.zeros_like(next_pmfs)
def project_to_bins(i, val):
val = val.at[i, l[i].astype(jnp.int32)].add(d_m_l[i])
val = val.at[i, u[i].astype(jnp.int32)].add(d_m_u[i])
return val
target_pmfs = jax.lax.fori_loop(0, target_pmfs.shape[0], project_to_bins, target_pmfs)
def loss(q_params, observations, actions, target_pmfs):
pmfs = q_network.apply(q_params, observations)
old_pmfs = pmfs[np.arange(pmfs.shape[0]), actions.squeeze()]
old_pmfs_l = jnp.clip(old_pmfs, a_min=1e-5, a_max=1 - 1e-5)
loss = (-(target_pmfs * jnp.log(old_pmfs_l)).sum(-1)).mean()
return loss, (old_pmfs * q_state.atoms).sum(-1)
(loss_value, old_values), grads = jax.value_and_grad(loss, has_aux=True)(
q_state.params, observations, actions, target_pmfs
)
q_state = q_state.apply_gradients(grads=grads)
return loss_value, old_values, q_state
start_time = time.time()
# TRY NOT TO MODIFY: start the game
obs, _ = envs.reset(seed=args.seed)
for global_step in range(args.total_timesteps):
# ALGO LOGIC: put action logic here
epsilon = linear_schedule(args.start_e, args.end_e, args.exploration_fraction * args.total_timesteps, global_step)
if random.random() < epsilon:
actions = np.array([envs.single_action_space.sample() for _ in range(envs.num_envs)])
else:
pmfs = q_network.apply(q_state.params, obs)
q_vals = (pmfs * q_state.atoms).sum(axis=-1)
actions = q_vals.argmax(axis=-1)
actions = jax.device_get(actions)
# TRY NOT TO MODIFY: execute the game and log data.
next_obs, rewards, terminations, truncations, infos = envs.step(actions)
# TRY NOT TO MODIFY: record rewards for plotting purposes
if "final_info" in infos:
for info in infos["final_info"]:
if info and "episode" in info:
print(f"global_step={global_step}, episodic_return={info['episode']['r']}")
writer.add_scalar("charts/episodic_return", info["episode"]["r"], global_step)
writer.add_scalar("charts/episodic_length", info["episode"]["l"], global_step)
# TRY NOT TO MODIFY: save data to reply buffer; handle `final_observation`
real_next_obs = next_obs.copy()
for idx, trunc in enumerate(truncations):
if trunc:
real_next_obs[idx] = infos["final_observation"][idx]
rb.add(obs, real_next_obs, actions, rewards, terminations, infos)
# TRY NOT TO MODIFY: CRUCIAL step easy to overlook
obs = next_obs
# ALGO LOGIC: training.
if global_step > args.learning_starts and global_step % args.train_frequency == 0:
data = rb.sample(args.batch_size)
loss, old_val, q_state = update(
q_state,
data.observations.numpy(),
data.actions.numpy(),
data.next_observations.numpy(),
data.rewards.numpy(),
data.dones.numpy(),
)
if global_step % 100 == 0:
writer.add_scalar("losses/loss", jax.device_get(loss), global_step)
writer.add_scalar("losses/q_values", jax.device_get(old_val.mean()), global_step)
print("SPS:", int(global_step / (time.time() - start_time)))
writer.add_scalar("charts/SPS", int(global_step / (time.time() - start_time)), global_step)
# update target network
if global_step % args.target_network_frequency == 0:
q_state = q_state.replace(target_params=optax.incremental_update(q_state.params, q_state.target_params, 1))
if args.save_model:
model_path = f"runs/{run_name}/{args.exp_name}.cleanrl_model"
model_data = {
"model_weights": q_state.params,
"args": vars(args),
}
with open(model_path, "wb") as f:
f.write(flax.serialization.to_bytes(model_data))
print(f"model saved to {model_path}")
from cleanrl_utils.evals.c51_jax_eval import evaluate
episodic_returns = evaluate(
model_path,
make_env,
args.env_id,
eval_episodes=10,
run_name=f"{run_name}-eval",
Model=QNetwork,
epsilon=0.05,
)
for idx, episodic_return in enumerate(episodic_returns):
writer.add_scalar("eval/episodic_return", episodic_return, idx)
if args.upload_model:
from cleanrl_utils.huggingface import push_to_hub
repo_name = f"{args.env_id}-{args.exp_name}-seed{args.seed}"
repo_id = f"{args.hf_entity}/{repo_name}" if args.hf_entity else repo_name
push_to_hub(args, episodic_returns, repo_id, "C51", f"runs/{run_name}", f"videos/{run_name}-eval")
envs.close()
writer.close()