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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,78 @@ | ||
| import jax | ||
| import jax.numpy as jnp | ||
| import gymnax | ||
|
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||
|
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||
| class EnvRollout(object): | ||
| def __init__( | ||
| self, | ||
| model_forward, | ||
| env_name: str = "Pendulum-v1", | ||
| num_env_steps: int = 200, | ||
| num_episodes: int = 20, | ||
| ): | ||
| """Wrapper to define batch evaluation for generation parameters.""" | ||
| self.env_name = env_name | ||
| self.num_env_steps = num_env_steps | ||
| self.num_episodes = num_episodes | ||
|
|
||
| # Define the RL environment & network forward function | ||
| self.env, self.env_params = gymnax.make(self.env_name) | ||
| self.model_forward = model_forward | ||
|
|
||
| # Set up the generation evaluation vmap-ed function - rl/supervised/etc. | ||
| self.gen_evaluate = self.batch_evaluate() | ||
|
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||
| def collect(self, rng_eval, policy_params): | ||
| """Reshape parameter vector and evaluate the generation.""" | ||
| # Reshape the parameters into the correct network format | ||
| rollout_keys = jax.random.split(rng_eval, self.num_episodes) | ||
|
|
||
| # Evaluate generation population on pendulum task - min cost! | ||
| pop_trajectories = self.gen_evaluate(rollout_keys, policy_params) | ||
| return pop_trajectories | ||
|
|
||
| def batch_evaluate(self): | ||
| """Evaluate a generation of networks on RL/Supervised/etc. task.""" | ||
| # vmap over different MC fitness evaluations for single network | ||
| batch_rollout = jax.jit( | ||
| jax.vmap(self.rollout, in_axes=(0, None), out_axes=0) | ||
| ) | ||
| return batch_rollout | ||
|
|
||
| def rollout(self, rng_input, policy_params): | ||
| """Rollout a pendulum episode with lax.scan.""" | ||
| # Reset the environment | ||
| rng_reset, rng_episode = jax.random.split(rng_input) | ||
| obs, state = self.env.reset(rng_reset, self.env_params) | ||
|
|
||
| def policy_step(state_input, tmp): | ||
| """lax.scan compatible step transition in jax env.""" | ||
| obs, state, policy_params, rng = state_input | ||
| rng, rng_step, rng_net = jax.random.split(rng, 3) | ||
| action = self.model_forward({"params": policy_params}, obs, rng=rng_net) | ||
| next_o, next_s, reward, done, _ = self.env.step( | ||
| rng_step, state, action, self.env_params | ||
| ) | ||
| carry = [next_o.squeeze(), next_s, policy_params, rng] | ||
| y = [next_o.squeeze(), reward, done] | ||
| return carry, y | ||
|
|
||
| # Scan over episode step loop | ||
| _, scan_out = jax.lax.scan( | ||
| policy_step, | ||
| [obs, state, policy_params, rng_episode], | ||
| [jnp.zeros((self.num_env_steps, self.input_shape[0] + 2))], | ||
| ) | ||
| # Return the sum of rewards accumulated by agent in episode rollout | ||
| obs, rewards, dones = scan_out[0], scan_out[1], scan_out[2] | ||
| rewards = rewards.reshape(self.num_env_steps, 1) | ||
| ep_mask = (jnp.cumsum(dones) < 1).reshape(self.num_env_steps, 1) | ||
| return obs, rewards, dones, jnp.sum(rewards * ep_mask) | ||
|
|
||
| @property | ||
| def input_shape(self): | ||
| """Get the shape of the observation.""" | ||
| rng = jax.random.PRNGKey(0) | ||
| obs, state = self.env.reset(rng, self.env_params) | ||
| return obs.shape |