Module AssetAllocator.algorithms.PPO.eval_policy
Expand source code
def _log_summary(ep_len, ep_ret, ep_num):
"""
Print to stdout what we've logged so far in the most recent episode.
Parameters:
None
Return:
None
"""
# Round decimal places for more aesthetic logging messages
ep_len = str(round(ep_len, 2))
ep_ret = str(round(ep_ret, 2))
# Print logging statements
print(flush=True)
print(f"-------------------- Episode #{ep_num} --------------------", flush=True)
print(f"Episodic Length: {ep_len}", flush=True)
print(f"Episodic Return: {ep_ret}", flush=True)
print(f"------------------------------------------------------", flush=True)
print(flush=True)
def rollout(policy, env, render):
"""
Returns a generator to roll out each episode given a trained policy and
environment to test on.
Parameters:
policy - The trained policy to test
env - The environment to evaluate the policy on
render - Specifies whether to render or not
Return:
A generator object rollout, or iterable, which will return the latest
episodic length and return on each iteration of the generator.
"""
# Rollout until user kills process
while True:
obs = env.reset()
done = False
# number of timesteps so far
t = 0
# Logging data
ep_len = 0 # episodic length
ep_ret = 0 # episodic return
while not done:
t += 1
# Render environment if specified, off by default
if render:
env.render()
# Query deterministic action from policy and run it
action = policy(obs).detach().numpy()
obs, rew, done, _ = env.step(action)
# Sum all episodic rewards as we go along
ep_ret += rew
# Track episodic length
ep_len = t
# returns episodic length and return in this iteration
yield ep_len, ep_ret
def eval_policy(policy, env, render=False):
"""
The main function to evaluate our policy with. It will iterate a generator object
"rollout", which will simulate each episode and return the most recent episode's
length and return. We can then log it right after. And yes, eval_policy will run
forever until you kill the process.
Parameters:
policy - The trained policy to test, basically another name for our actor model
env - The environment to test the policy on
render - Whether we should render our episodes. False by default.
Return:
None
NOTE: To learn more about generators, look at rollout's function description
"""
# Rollout with the policy and environment, and log each episode's data
for ep_num, (ep_len, ep_ret) in enumerate(rollout(policy, env, render)):
_log_summary(ep_len=ep_len, ep_ret=ep_ret, ep_num=ep_num)Functions
def eval_policy(policy, env, render=False)-
The main function to evaluate our policy with. It will iterate a generator object "rollout", which will simulate each episode and return the most recent episode's length and return. We can then log it right after. And yes, eval_policy will run forever until you kill the process.
Parameters
policy - The trained policy to test, basically another name for our actor model env - The environment to test the policy on render - Whether we should render our episodes. False by default.
Return
None
NOTE: To learn more about generators, look at rollout's function description
Expand source code
def eval_policy(policy, env, render=False): """ The main function to evaluate our policy with. It will iterate a generator object "rollout", which will simulate each episode and return the most recent episode's length and return. We can then log it right after. And yes, eval_policy will run forever until you kill the process. Parameters: policy - The trained policy to test, basically another name for our actor model env - The environment to test the policy on render - Whether we should render our episodes. False by default. Return: None NOTE: To learn more about generators, look at rollout's function description """ # Rollout with the policy and environment, and log each episode's data for ep_num, (ep_len, ep_ret) in enumerate(rollout(policy, env, render)): _log_summary(ep_len=ep_len, ep_ret=ep_ret, ep_num=ep_num) def rollout(policy, env, render)-
Returns a generator to roll out each episode given a trained policy and environment to test on.
Parameters
policy - The trained policy to test env - The environment to evaluate the policy on render - Specifies whether to render or not
Return
A generator object rollout, or iterable, which will return the latest episodic length and return on each iteration of the generator.
Expand source code
def rollout(policy, env, render): """ Returns a generator to roll out each episode given a trained policy and environment to test on. Parameters: policy - The trained policy to test env - The environment to evaluate the policy on render - Specifies whether to render or not Return: A generator object rollout, or iterable, which will return the latest episodic length and return on each iteration of the generator. """ # Rollout until user kills process while True: obs = env.reset() done = False # number of timesteps so far t = 0 # Logging data ep_len = 0 # episodic length ep_ret = 0 # episodic return while not done: t += 1 # Render environment if specified, off by default if render: env.render() # Query deterministic action from policy and run it action = policy(obs).detach().numpy() obs, rew, done, _ = env.step(action) # Sum all episodic rewards as we go along ep_ret += rew # Track episodic length ep_len = t # returns episodic length and return in this iteration yield ep_len, ep_ret