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import torch
def batch_simulate_on_environment(policy, env, verbose = True):
if verbose:
print("*** In batch_simulate_on_environment ***")
from Dataset import Trajectory, TrajectoryDataset
from math import ceil
dataset = TrajectoryDataset()
trajectories = [Trajectory() for _ in range(env.bsize)]
batch_obs = env.reset()
batch_done = [False,]*env.bsize
while not all(batch_done):
with torch.no_grad():
actions = policy(batch_obs)
batch_feedback = env.step(actions)
for i, feedback in zip(range(env.bsize), batch_feedback):
if feedback is None:
continue
next_obs, r, done = feedback
trajectories[i].append({"observation": batch_obs[i],
"action": actions[i],
"reward": r,
"next_observation": next_obs,
"done": done,
})
batch_obs[i] = next_obs
batch_done[i] = done
for trajectory in trajectories:
dataset.append_trajectory(trajectory)
print(trajectory.transitions[-1].next_observation)
dataset.check_consistency()
if verbose:
print("Data Coollection is Complete. Returns: \n", dataset.get_all_trajectory_returns(), "\n with mean: ",dataset.mean_trajectory_return(), "\n" )
return dataset
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