From 74b64637c043c41641debd0f66eaa7fe8bb2ddeb Mon Sep 17 00:00:00 2001 From: Albert Alonso Date: Tue, 26 Sep 2023 14:36:10 +0200 Subject: [PATCH] upload training and auxiliary files --- .gitignore | 41 +++ README.md | 47 +++ chemoxrl/__init__.py | 9 + chemoxrl/cell.py | 160 +++++++++ chemoxrl/rppo.py | 338 ++++++++++++++++++ chemoxrl_aux/__init__.py | 0 chemoxrl_aux/evaluate.py | 35 ++ chemoxrl_aux/loader.py | 90 +++++ chemoxrl_aux/policies.py | 63 ++++ .../checkpoint_1992294400 | Bin 0 -> 114098 bytes .../config.json | 23 ++ .../env_params.json | 12 + .../checkpoint_1912602624 | Bin 0 -> 104879 bytes .../config.json | 23 ++ .../env_params.json | 12 + .../checkpoint_2818572288 | Bin 0 -> 114098 bytes .../config.json | 23 ++ .../env_params.json | 12 + .../checkpoint_272629760 | Bin 0 -> 114098 bytes .../config.json | 23 ++ .../env_params.json | 12 + .../checkpoint_1115684864 | Bin 0 -> 104879 bytes .../config.json | 23 ++ .../env_params.json | 12 + .../checkpoint_273678336 | Bin 0 -> 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+**/results.pkl +**/data.json + +# Ignore cache folders +.cache/ + +# Ignore not optimal checkpoints and logs +logs/ +models/**/pre/ +models/**/post/ +models/**/events** diff --git a/README.md b/README.md new file mode 100644 index 0000000..8ae9e5e --- /dev/null +++ b/README.md @@ -0,0 +1,47 @@ +# Chemotaxis & Reinforcement learning: spatial-temporal information optimal integration + +This is the repository for the project of studying chemotaxis optimal strategies +when the agent uses both temporal and spatial information. + +## File Structure + +The project is organized as follows: + +``` +├── README.md +├── chemoxrl/ # Folder containing the code related to training the agents and env. +├── chemoxrl_aux/ # Utils functions used in analysis but not needed for training +├── models/ # Trained agents weights. +├── pyproject.toml # Package setup for both chemoxrl and chemoxrl[aux] +├── requirements.txt # Project dependencies +└── train.py # Script to train the agent. +``` + +## Setup + +Create a virtual environment and install the requiered packages (Note: we use `jaxlib` for GPU) + +``` +python3 -m venv venv +source venv/bin/activate +pip install -r requirements.txt +``` + +Likewise, we recommend to install this library locally + +``` +pip install -e . +``` + +## Usage + +One can train the agent by using + +``` +python3 train.py +``` + The available options can be seen by using + +``` +python3 train.py --help +``` diff --git a/chemoxrl/__init__.py b/chemoxrl/__init__.py new file mode 100644 index 0000000..5a1e001 --- /dev/null +++ b/chemoxrl/__init__.py @@ -0,0 +1,9 @@ +from . import rppo +from .rppo import ExperimentConfig, ActorCritic +from .cell import EnvParams + +__all__ = [ + "ExperimentConfig", + "ActorCritic", + "EnvParams", +] diff --git a/chemoxrl/cell.py b/chemoxrl/cell.py new file mode 100644 index 0000000..2b380f0 --- /dev/null +++ b/chemoxrl/cell.py @@ -0,0 +1,160 @@ +from functools import partial +from typing import Union + +from flax.struct import dataclass +import jax +import jax.numpy as jnp + + +@dataclass +class EnvParams: + max_steps_in_episode: int = 256 + radius: float = 4 # Radius of the cell (μm). + n_receptors: int = 8 # Number of discrete receptors along surface. + speed: float = 4 # Swimming speed (μm/s). + rotational_diffusion: float = 0.025 # Rotational diffusion constant (1/s). + decay_rate: float = 0.01 # Average decay rate for the simulation. + diffusion_coeff: float = 100 # Average diffusion coefficient for the simulation. + dt: float = 0.1 # Time step of the simulation. + C_min: int = 4 # Minimum value of the exponent for the concentration. + C_max: int = 5 # Maximum value of the exponent for the concentratino. + + +@dataclass +class CellState: + step: int # Step count in the episode. + x: jax.Array # Coordinates of the cell. + theta: jax.Array # Orientation of the cell. + cum_reward: float # Cumulative reward at R(t<=time) + N: Union[jax.Array, int] # Number of particles + d_init: jax.Array # Initial distance to the center (useful for reward) + + +def polar_to_cartesian(r, phi): + x = r * jnp.cos(phi) + y = r * jnp.sin(phi) + return jnp.array([x, y]) + + +def cartesian_to_polar(x, y): + r = jnp.sqrt(x**2 + y**2) + phi = jnp.arctan2(y, x) + return r, phi + + +def gradient(env_params): + return jnp.sqrt(env_params.decay_rate / env_params.diffusion_coeff) + + +class Cell: + @partial(jax.jit, static_argnums=(0, 2)) + def reset(self, rng, env_params): + rng_media, rng_cell = jax.random.split(rng, 2) + Cs = jnp.logspace(env_params.C_min, env_params.C_max, num=100, dtype=jnp.int32) + N = jax.random.choice(rng_cell, Cs, shape=()) + + x, theta = self._init_cell(rng_cell, env_params) + d_init = jnp.hypot(x[0], x[1]) + state = CellState(step=0, x=x, theta=theta, cum_reward=0.0, N=N, d_init=d_init) + + obs = self._get_obs(rng_media, state, env_params) + obs = jnp.concatenate((obs, jnp.zeros(1))) + return obs, state + + @partial(jax.jit, static_argnums=(0, 4)) + def step(self, rng, state, action, env_params): + # Steps returns values until a done happens. Then it returns 0s. + # until the next self.reset is called. + obs_st, state_st, reward, done = self._step(rng, state, action, env_params) + state_re = jax.tree_util.tree_map(lambda x: x*0, state_st) + state = jax.tree_util.tree_map(lambda x, y: jax.lax.select(done, x, y), state_re, state_st) + obs_re = jnp.zeros(self.observation_space(env_params).shape) + obs = jax.lax.select(done, obs_re, obs_st) + return obs, state, reward, done, {} + + def _init_cell(self, rng, env_params): + rng_theta, rng_r, rng_phi = jax.random.split(rng, num=3) + theta = jax.random.uniform(rng_theta) * 2 * jnp.pi + percentile = jax.random.uniform(rng_r, minval=0.3, maxval=0.5) + r = -jnp.log(1 - percentile) / gradient(env_params) + phi = jax.random.uniform(rng_phi) * 2 * jnp.pi + x = polar_to_cartesian(r, phi) + return x, theta + + def _step(self, rng, state, action, env_params): + rng_o, rng_a = jax.random.split(rng, 2) + noise = jax.random.normal(rng_a) * jnp.sqrt(2 * env_params.rotational_diffusion * env_params.dt) + dtheta = action[0] * jnp.pi + noise + theta = (state.theta + dtheta) % (2 * jnp.pi) + v = polar_to_cartesian(env_params.speed, theta) + x = state.x + v * env_params.dt + state = state.replace(x=x, theta=theta) + + obs = self._get_obs(rng_o, state, env_params) + state = state.replace(step=state.step + 1) + + final_reward = self._get_reward(state, env_params) + has_reached = jnp.hypot(x[0], x[1]) <= (-jnp.log(0.9) / gradient(env_params)) + done = (state.step >= env_params.max_steps_in_episode) | has_reached + reward = jax.lax.select(done, final_reward, 0.0) + state = state.replace(cum_reward=state.cum_reward + reward) + + obs = jnp.concatenate((obs, action)) + return obs, state, reward, done + + def _get_obs(self, rng, state, env_params): + M = env_params.n_receptors + a = env_params.radius + + angles = jnp.arange(M) * ((2 * jnp.pi) / M) + state.theta + receptors = state.x + a * jnp.array([jnp.cos(angles), jnp.sin(angles)]).T + sensor_area = (a * jnp.sin(jnp.pi / M)) ** 2 * jnp.pi + B = (state.N / (2 * jnp.pi)) * sensor_area # integration constant + rate = gradient(env_params) + + @jax.vmap + def detect(rng, xi): + d = jnp.hypot(xi[0], xi[1]) + c = rate * jnp.exp(-rate * d) + M_avg = B * c + M = jax.random.poisson(rng, M_avg) + m = jnp.log(M + 1) + return m + + m = detect(jax.random.split(rng, M), receptors) + return m + + def _get_reward(self, state, env_params): + max_steps = env_params.max_steps_in_episode + d = jnp.hypot(state.x[0], state.x[1]) + d_min = -jnp.log(0.9) / gradient(env_params) + distance_reward = jnp.clip((d_min - d) / (state.d_init - d_min), -1.0, 0.0) + time_reward = jnp.clip((max_steps - state.step) / max_steps, 0.0, 1.0) + return distance_reward + time_reward + + @property + def num_actions(self): + return 1 + + def observation_space(self, env_params): + return jnp.empty(shape=(env_params.n_receptors + 1,)) + + +class MultiCell: + def __init__(self, env_params, n_envs): + self.env = Cell() + self.env_params = env_params + self.n_envs = n_envs + self.num_actions = self.env.num_actions + self.observation_space = self.env.observation_space + + @partial(jax.jit, static_argnums=0) + def reset(self, rng): + rngs = jax.random.split(rng, self.n_envs) + return jax.vmap(self.env.reset, in_axes=(0, None))(rngs, self.env_params) + + @partial(jax.jit, static_argnums=0) + def step(self, rng, env_state, actions): + rngs = jax.random.split(rng, self.n_envs) + batched_step = jax.vmap(self.env.step, in_axes=(0, 0, 0, None)) + return batched_step(rngs, env_state, actions, self.env_params) diff --git a/chemoxrl/rppo.py b/chemoxrl/rppo.py new file mode 100644 index 0000000..d9bed0f --- /dev/null +++ b/chemoxrl/rppo.py @@ -0,0 +1,338 @@ +from functools import partial +from typing import NamedTuple + +from flax.core.frozen_dict import freeze +import flax.linen as nn +from flax.training import checkpoints +from flax.training.train_state import TrainState +import jax +import jax.numpy as jnp +import optax +from tqdm import tqdm + +from chemoxrl.cell import MultiCell + + +class ExperimentConfig(NamedTuple): + n_train_envs: int = 256 # Number of train environments in parallel. + total_steps: int = int(10e6) # Number of train steps. + n_steps: int = 301 # Number of steps per environment before training. + max_grad_norm: float = 0.5 # Global norm to clip gradients by. + eval_interval: int = 1_000_000 # Number of steps until an evaluation is done. + n_eval_envs: int = 2048 # Number of parallel envs to evaluate on. + learning_rate: float = 3e-4 # Learning rate. + n_epochs: int = 4 # Number of epochs at each training step. + n_minibatch: int = 4 # Number of minibatches to split the buffer into. + clip_eps: float = 0.2 # Surrogate clipping loss. + entropy_coeff: float = 0.00 # Entropy loss coefficient + critic_coeff: float = 0.5 # Value loss coefficient + discount: float = 0.99 # Discount factor for the GAE calculation. + gae_lambda: float = 0.95 # "GAE lambda" + logdir: str = "./logs/" # Path to store the logs. + load: str = "" # Load a previous policy before training. + seed: int = 118 # Random state seed. + hidden_cells: int = 25 # Neurons in the recurrent network. + spatial: bool = True + memory: bool = True + recurrent: bool = True + + +class Rollout(NamedTuple): + states: jax.Array + actions: jax.Array + rewards: jax.Array + dones: jax.Array + log_probs: jax.Array + values: jax.Array + hidden_state: jax.Array + mask: jax.Array + + +class MemoryCell(nn.Module): + decay: float = 0.1 + + @nn.vmap + @nn.compact + def __call__(self, h, x): + x = jnp.concatenate((x, x.mean(keepdims=True))) + h = h.reshape(-1, 3) + + k = self.decay + h_new = jnp.empty_like(h) + h_new = h_new.at[:, 0].set(k * x + (1 - k) * h[:, 0]) + h_new = h_new.at[:, 1].set(k * h[:, 0] + (1 - k) * h[:, 1]) + h_new = h_new.at[:, 2].set(k * h[:, 1] + (1 - k) * h[:, 2]) + + # Normalize + x_normalized = x.at[:-1].add(-x[-1]) + h_new_normalized = h_new.at[:-1, :].add(-h_new[-1, :]) + h_new_normalized = h_new_normalized.at[-1, 0].add(-x[-1]) + h_new_normalized = h_new_normalized.at[-1, 1].add(-h_new[-1, 0]) + h_new_normalized = h_new_normalized.at[-1, 2].add(-h_new[-1, 1]) + + h_new = h_new.flatten() + h_new_normalized = h_new_normalized.flatten() + x_input = jnp.concatenate((x_normalized, h_new_normalized)) + return h_new, (h_new, x_input) + + +class ActorCritic(nn.Module): + num_output_units: int + num_hidden_units: int = 64 + num_hidden_layers: int = 2 + min_std: float = 0.05 + max_std: float = 1.0 + spatial: bool = True + memory: bool = False + recurrent: bool = False + + @nn.compact + def __call__(self, xs, h_init): + init_fn = nn.initializers.orthogonal(scale=jnp.sqrt(2)) + init_fn_actor = nn.initializers.orthogonal(scale=0.01) + + if not self.spatial: + # All input sensors report the mean (last element is previous action) + xs = xs.at[..., :-1].set(xs[..., :-1].mean(axis=-1, keepdims=True)) + + if self.memory: + if self.recurrent: + F = nn.scan(nn.GRUCell, variable_broadcast="params", split_rngs={"params": False}) + _, hs = F()(h_init, xs) + x_input = hs + else: + xs = xs[..., :-1] # remove the action from the input. + F = nn.scan(MemoryCell) + _, (hs, x_input) = F()(h_init, xs) + else: + hs = nn.Dense(h_init.shape[-1], kernel_init=init_fn, name='feature_ext')(xs) + x_input = hs + + # Critic network + x_v = x_input + for _ in range(self.num_hidden_layers): + x_v = nn.Dense(self.num_hidden_units*2, kernel_init=init_fn)(x_v) + x_v = nn.tanh(x_v) + value = nn.Dense(1, kernel_init=nn.initializers.orthogonal())(x_v) + + # Actor Network + x_a = x_input + for _ in range(self.num_hidden_layers): + x_a = nn.Dense(self.num_hidden_units, kernel_init=init_fn)(x_a) + x_a = nn.tanh(x_a) + + mu = nn.Dense(self.num_output_units, kernel_init=init_fn_actor)(x_a) + log_scale = nn.Dense(self.num_output_units, kernel_init=init_fn_actor)(x_a) + scale = jax.lax.clamp(self.min_std, jax.nn.softplus(-0.5 + log_scale), self.max_std) + return value, (mu, scale), hs + + +def loss_fn(params, apply_fn, minibatch, eps=0.2, entropy_coeff=0.001, vf_coeff=0.5): + s, a, logp_old, target, A, h, mask = minibatch + values, (mu, scale), _ = apply_fn(params, s, h[0]) + values = values[..., 0] + num_entries = jnp.sum(mask) + + # Compute Clipped Surrogate Loss on the policy. + # Normalize the advantage seems to help. + Am = A * mask + Amean = jnp.sum(Am) / num_entries + Astd = jnp.sqrt(jnp.sum(Am**2) / num_entries - Amean**2) + A = (A - Amean) / (Astd + 1e-8) + logp = jax.scipy.stats.norm.logpdf(a, loc=mu, scale=scale).sum(-1) + ratio = jnp.exp(logp - logp_old) + policy_loss = jnp.minimum(ratio * A, jnp.clip(ratio, 1.0 - eps, 1.0 + eps) * A) + policy_loss = -jnp.sum(mask * policy_loss) / num_entries + + # Critic MSE loss. + value_loss = jnp.sum(mask * (values - target) ** 2) / num_entries + + # Entropy of normal distribution is H = -p*ln(p) = 1/2 ln(e*2π*σ^2) + entropy = jnp.sum(0.5 + 0.5 * jnp.log(2 * jnp.pi) + jnp.log(scale), axis=-1) + entropy_loss = jnp.sum(mask * entropy) / num_entries + + # Compute KL divergence (approximation) + approx_kl_div = jnp.sum(mask * ((ratio - 1) - (logp - logp_old))) / num_entries + + loss = policy_loss + vf_coeff * value_loss - entropy_coeff * entropy_loss + aux = (loss, policy_loss, value_loss, entropy_loss, approx_kl_div) + return loss, aux + + +@partial(jax.jit, static_argnums=(3, 4)) +def train_step(rng, train_state, batch, n_epochs=8, n_minibatch=8): + + buffer_size = batch[0].shape[1] + + grad_fn = jax.grad(loss_fn, has_aux=True) + + def epoch_step(state, rng): + def batch_step(state, chosen): + minibatch = jax.tree_util.tree_map(lambda x: x[:, chosen], batch) + grads, metrics = grad_fn(state.params, state.apply_fn, minibatch) + state = state.apply_gradients(grads=grads) + return state, metrics + + batch_indices = jax.random.permutation(rng, buffer_size) + batch_indices = batch_indices.reshape(n_minibatch, -1) + state, metrics = jax.lax.scan(batch_step, init=state, xs=batch_indices) + return state, jax.tree_util.tree_map(jnp.mean, metrics) + + rngs = jax.random.split(rng, n_epochs) + train_state, metrics = jax.lax.scan(epoch_step, init=train_state, xs=rngs) + return train_state, jax.tree_util.tree_map(jnp.mean, metrics) + + +def calculate_gae(values, rewards, dones, discount=0.99, A_lambda=0.95): + def body_fn(A, x): + next_value, done, value, reward = x + value_diff = discount * next_value * (1 - done) - value + delta = reward + value_diff + A = delta + discount * A_lambda * (1 - done) * A + return A, A + + xs = (values[1:], dones[:-1], values[:-1], rewards[:-1]) + num_envs = values.shape[1] + _, gae = jax.lax.scan(body_fn, jnp.zeros(num_envs), xs, reverse=True) + gae = jnp.pad(gae, pad_width=((0, 1), (0, 0))) + return gae + + +@partial(jax.jit, static_argnums=(1, 2, 3)) +def collect_batch(buffer, discount=0.99, A_lambda=0.95, permutate=True): + gae = calculate_gae(buffer.values, buffer.rewards, buffer.dones, discount, A_lambda) + target = gae + buffer.values + batch = (buffer.states, buffer.actions, buffer.log_probs, target, gae, buffer.hidden_state, buffer.mask) + + # Remove the last element of the batch since it doesn't have advantage to compute. + batch = jax.tree_util.tree_map(lambda x: x[:-1], batch) + + if permutate: + batch = jax.tree_util.tree_map(lambda x: x.reshape(-1, *x.shape[2:]), batch) + batch = jax.tree_util.tree_map(lambda x: x[None, ...], batch) + + return batch + + +def init_train_state(rng, env, env_params, config): + model = ActorCritic(num_output_units=env.num_actions, + spatial=config.spatial, + memory=config.memory, + recurrent=config.recurrent) + + obs_shape = env.observation_space(env_params).shape + dummy_x = jnp.ones((1, 1, *obs_shape)) + hidden_state = jnp.zeros((1, config.hidden_cells)) + params = model.init(rng, dummy_x, hidden_state) + + if config.load: + # Overwrite the initial parameters with the loaded ones but + # leave the scale layer as initialised. + ckpt = checkpoints.restore_checkpoint(config.load, target=None) + new_params = {**params, **ckpt['params']} + new_params['params']['Dense_6'] = params['params']['Dense_6'] + params = freeze(new_params) + + tx = optax.adam(config.learning_rate, eps=1e-7) + return TrainState.create(apply_fn=model.apply, params=params, tx=tx) + + +@partial(jax.jit, static_argnums=(1, 2, 3, 4)) +def evaluate(state, env_params, num_envs=1024, hidden_size=24, deterministic=True): + eval_envs = MultiCell(env_params, num_envs) + eval_rng = jax.random.PRNGKey(0) + + def transition_step(carry, rng): + s, env_state, h = carry + rng_action, rng_step = jax.random.split(rng, 2) + out = state.apply_fn(state.params, s[None, ...], h) + _, (mu, scale), next_h = jax.tree_util.tree_map(lambda x: x[-1], out) + a = jax.random.normal(rng_action, shape=mu.shape) * scale + mu + a = jax.lax.select(deterministic, mu, a) + next_s, next_env_state, r, done, __ = eval_envs.step(rng_step, env_state, a) + return (next_s, next_env_state, next_h), (r, done) + + obs, env_state = eval_envs.reset(eval_rng) + h = jnp.zeros((*obs.shape[:-1], hidden_size)) + rngs = jax.random.split(eval_rng, env_params.max_steps_in_episode) + _, (rewards, dones) = jax.lax.scan(transition_step, (obs, env_state, h), rngs) + + end_step = jax.vmap(lambda x: jnp.argwhere(x, size=1)[...,0], in_axes=1)(dones)[...,0] + mean_reward = rewards[end_step, jnp.arange(num_envs)].mean() + reached_percentage = (end_step < env_params.max_steps_in_episode-1).mean() + arrival_time = end_step * env_params.dt + return mean_reward, reached_percentage, arrival_time + + +def train_loop(rng, config, env_params, checkpointer): + + # Initialise the batched environment. + envs = MultiCell(env_params, config.n_train_envs) + + # Initialise the network + rng, rng_init = jax.random.split(rng, num=2) + train_state = init_train_state(rng_init, envs, env_params, config) + + @jax.jit + def run_episodes(train_state, rng): + + def step(carry, rng): + train_state, obs, env_state, h, mask = carry + rng_action, rng_step = jax.random.split(rng, 2) + out = train_state.apply_fn(train_state.params, obs[None, ...], h) + value, (mu, scale), next_h = jax.tree_util.tree_map(lambda x: x[-1], out) + action = jax.random.normal(rng_action, shape=mu.shape) * scale + mu + log_prob = jax.scipy.stats.norm.logpdf(action, mu, scale).sum(-1) + next_obs, next_env_state, reward, done, _ = envs.step(rng_step, env_state, action) + rollout = Rollout(obs, action, reward, done, log_prob, value[..., 0], h, 1-mask) + return (train_state, next_obs, next_env_state, next_h, mask | done), rollout + + rng, rng_reset = jax.random.split(rng, 2) + rngs = jax.random.split(rng, env_params.max_steps_in_episode+1) + + obs, env_state = envs.reset(rng_reset) + hidden_state = jnp.zeros(shape=(*obs.shape[:-1], config.hidden_cells)) + mask = jnp.zeros(config.n_train_envs) > 0 + init = (train_state, obs, env_state, hidden_state, mask) + _, buffer = jax.lax.scan(step, init, rngs) + return buffer + + step_size = int(config.n_train_envs * config.n_steps) + eval_interval = max(1, int(config.total_steps / 100 // step_size)) + pbar = tqdm(total=config.total_steps, dynamic_ncols=True) + + steps_passed = 0 + for step in range(config.total_steps // step_size): + + # Perform n_steps for each of the num_train_envs in parallel. + buffer = run_episodes(train_state, jax.random.fold_in(rng, step)) + steps_passed += step_size + + # Collect the buffer from the rollout and perform a training step corresponding + # to n_minibatch*n_epochs gradient descent steps with the current policy. + rng_train, rng = jax.random.split(rng, 2) + batch = collect_batch(buffer, config.discount, config.gae_lambda, not config.memory) + train_state, metrics = train_step(rng_train, train_state, batch, config.n_epochs, config.n_minibatch) + metrics = jax.tree_util.tree_map(jnp.mean, metrics) + + logging_step = int(step * step_size) + checkpointer.writer.scalar("train/loss", metrics[0], logging_step) + checkpointer.writer.scalar("train/policy_loss", metrics[1], logging_step) + checkpointer.writer.scalar("train/critic_loss", metrics[2], logging_step) + checkpointer.writer.scalar("train/entropy_loss", metrics[3], logging_step) + checkpointer.writer.scalar("train/kl_divergence", metrics[4], logging_step) + + if step % eval_interval == 0: + eval_metrics = evaluate(train_state, env_params, config.n_eval_envs, config.hidden_cells, False) + metrics = jax.tree_util.tree_map(jnp.mean, eval_metrics) + checkpointer.writer.scalar("eval/reward", metrics[0], logging_step) + checkpointer.writer.scalar("eval/reached", metrics[1], logging_step) + checkpointer.writer.scalar("eval/ftp", metrics[2], logging_step) + pbar.set_description(f"R: {metrics[0]:.2f}({metrics[1]:.2f})") + pbar.update(steps_passed) + steps_passed = 0 + + ckpt = {"params": train_state.params} + checkpoints.save_checkpoint(checkpointer.dir, target=ckpt, step=logging_step, keep_every_n_steps=int(1e6), keep=10) + + return train_state diff --git a/chemoxrl_aux/__init__.py b/chemoxrl_aux/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/chemoxrl_aux/evaluate.py b/chemoxrl_aux/evaluate.py new file mode 100644 index 0000000..c01d9aa --- /dev/null +++ b/chemoxrl_aux/evaluate.py @@ -0,0 +1,35 @@ +import jax +import jax.numpy as jnp +import numpy as np + +from chemoxrl.cell import MultiCell + +def evaluate_policy(model, num_envs, seed): + rng, rng_reset = jax.random.split(jax.random.PRNGKey(seed)) + eval_envs = MultiCell(model.env_params, num_envs) + + @jax.jit + def transition_step(carry, rng): + s, env_state, h = carry + rng_action, rng_step = jax.random.split(rng, 2) + a, h = model.policy(rng_action, s, h) + next_s, next_env_state, _, done, __ = eval_envs.step(rng_step, env_state, a) + h = jnp.where(done[:, None], jnp.zeros_like(h), h) + return (next_s, next_env_state, h), done + + obs, env_state = eval_envs.reset(rng_reset) + h = jnp.zeros((*obs.shape[:-1], model.hidden_size)) + rngs = jax.random.split(rng, model.env_params.max_steps_in_episode) + _, dones = jax.lax.scan(transition_step, init=(obs, env_state, h), xs=rngs) + + # Compute the arrival times of the simulations + last_step = jnp.argmax(dones, axis=0) + times = last_step[last_step > 0] * model.env_params.dt + + # Compute the chemotatic efficiency of the policy + rate = np.sqrt(model.env_params.decay_rate / model.env_params.diffusion_coeff) + delta = -np.log(0.9) / rate + d_init = np.sqrt(np.sum(env_state.x**2, axis=-1)) + efficiencies = (d_init - delta) / (model.env_params.speed * times) + + return times, efficiencies diff --git a/chemoxrl_aux/loader.py b/chemoxrl_aux/loader.py new file mode 100644 index 0000000..22ca711 --- /dev/null +++ b/chemoxrl_aux/loader.py @@ -0,0 +1,90 @@ +from dataclasses import asdict +import json +import pathlib +from typing import Callable, List, NamedTuple + +from flax.training import checkpoints +import pick +from chemoxrl import EnvParams, ExperimentConfig +from chemoxrl_aux.policies import greedy_policy, rppo_policy + + +class CkptModel(NamedTuple): + name: str + label: str + env_params: EnvParams + hidden_size: int + policy: Callable + + +def make_pickable(model: CkptModel): + env_params = asdict(model.env_params) + _model = {"name": model.name, "label": model.label, "env_params": env_params} + return _model + + +MODEL_LABELS = { + (True, True, True): "Combined", + (True, False, False): "Spatial", + (True, False, True): "Spatial", + (False, True, True): "Temporal", + (False, True, False): "Temporal(Markovian)", + (True, True, False): "Combined (Markovian)", +} + + +def load_ckpt(ckpt, deterministic=True, max_steps=0) -> CkptModel: + path = pathlib.Path(ckpt) + + with open(path / "env_params.json", "r") as f: + args = json.load(f) + + with open(path / "config.json", "r") as f: + args.update(json.load(f)) + + env_args = {k: v for k, v in args.items() if k in EnvParams.__dict__} + if max_steps > 0: + env_args["max_steps_in_episode"] = max_steps + env_params = EnvParams(**env_args) + ec_args = {k: v for k, v in args.items() if k in ExperimentConfig.__dict__} + config = ExperimentConfig(**ec_args) + params = checkpoints.restore_checkpoint(path, target=None)["params"] + name = path.name + label = MODEL_LABELS[(config.spatial, config.memory, config.recurrent)] + policy = rppo_policy(params, deterministic, config.spatial, config.memory, config.recurrent) + return CkptModel(name, label, env_params, config.hidden_cells, policy) + + +def load_options(checkpoints_dir, pattern="*"): + root = pathlib.Path(checkpoints_dir) + directories = list(root.glob(pattern)) + directories = sorted(directories, key=lambda f: f.stat().st_ctime, reverse=True) + labels = [f"{o.stem} ({len(list(o.glob('checkpoint_*')))} checkpoints)" for o in directories] + return directories, labels + + +def load_multi_interactive(base_dir, pattern="*", select_all=False, **kwargs) -> List[CkptModel]: + directories, labels = load_options(base_dir, pattern) + if select_all: + selected = directories + else: + selected = pick.pick(labels, title="Choose checkpoint(s) to load", multiselect=True) + selected = [directories[s[1]] for s in selected] + results = [load_ckpt(s, **kwargs) for s in selected] + return results + + +def load_multi(base_dir, pattern="*", **kwargs) -> List[CkptModel]: + selected, labels = load_options(base_dir, pattern) + results = [load_ckpt(s, **kwargs) for s in selected] + return results + + +def load_greedy(env_params, step, epsilon, kernel, adapt): + name = f"greedy_{kernel}_{epsilon:.4f}_{step:04d}" + "_adapt" if adapt else "" + label = rf"{kernel}{'(adaptive)' if adapt else ''} T={step:1d} eps={epsilon:.2f}" + policy, hidden_size = greedy_policy( + env_params, steps=step, eps=epsilon, kernel=kernel, adapt=adapt + ) + model = CkptModel(name, label, env_params, hidden_size, policy) + return model diff --git a/chemoxrl_aux/policies.py b/chemoxrl_aux/policies.py new file mode 100644 index 0000000..13ad2a1 --- /dev/null +++ b/chemoxrl_aux/policies.py @@ -0,0 +1,63 @@ +import jax +import jax.numpy as jnp + +import chemoxrl +from chemoxrl.cell import Cell + + +def random_walk_policy(n=4): + options = jnp.linspace(0, n, endpoint=False) / n + + def policy(rng, _, h): + dtheta = jax.random.choice(rng, options) + return (jnp.array([dtheta]), h) + + return jax.jit(jax.vmap(policy, in_axes=(None, 0, 0))), 1 + + +def greedy_policy(env_params, eps=0.1, steps=1, kernel="lin", adapt=False, gamma=1.0,): + M = env_params.n_receptors + phi = jnp.arange(M) * 2 * jnp.pi / M + + hidden_size = steps * M + + if kernel == "exp": + K = jnp.exp(-gamma*jnp.arange(steps))[:, jnp.newaxis] + else: + K = jnp.ones((steps, 1)) + + interp = jax.vmap(lambda x, c: jnp.interp(phi, x, c, period=(2 * jnp.pi)), in_axes=(None, 0)) + + def policy(rng, s, h): + m = s[:M] + h = jnp.reshape(h, (steps, M)) + if adapt: + theta = phi - s[-1] * jnp.pi + h = interp(theta, h) + + h = jnp.roll(h, 1, axis=0) + h = h.at[0].set(m) + weights = jnp.mean(h*K, axis=0) + ex = jnp.sum(jnp.cos(phi) * weights) + ey = jnp.sum(jnp.sin(phi) * weights) + dtheta = jnp.arctan2(ey, ex) / jnp.pi + dtheta = jnp.clip(dtheta, -eps, eps) + a = jnp.array([dtheta]) + return a, h.flatten() + + _select_action_fn = jax.vmap(policy, in_axes=(None, 0, 0)) + _select_action_fn = jax.jit(_select_action_fn) + return _select_action_fn, hidden_size + + +def rppo_policy(params, deterministic=False, spatial=False, memory=False, recurrent=False): + model = chemoxrl.ActorCritic(Cell().num_actions, spatial=spatial, memory=memory, recurrent=recurrent) + + def policy(rng, s, h): + outs = model.apply(params, s[None], h) + _, (mu, scale), h = jax.tree_util.tree_map(lambda x: x[-1], outs) + a = jax.random.normal(rng, shape=mu.shape) * scale + mu + a = jax.lax.select(deterministic, mu, a) + return a, h + + return jax.jit(policy) diff --git a/models/PPO_R05_SF_MT_4096_20230704-0845_42/checkpoint_1992294400 b/models/PPO_R05_SF_MT_4096_20230704-0845_42/checkpoint_1992294400 new file mode 100644 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