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Last active April 19, 2021 08:54
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Mixing Reinforcement Learning (RL) and Evolution Strategy (ES) using Stable-Baselines
import gym
import numpy as np
import cma
from collections import OrderedDict
from stable_baselines import A2C
def flatten(params):
:param params: (dict)
:return: (np.ndarray)
params_ = []
for key in params.keys():
return np.concatenate(params_)
def to_dict(flat_vec, params):
:param flat_vec: (np.ndarray)
:param params: (OrderedDict)
:return: (OrderedDict)
params_ = OrderedDict()
start_idx = 0
for key in params.keys():
n_elem = params[key].size
params_[key] = flat_vec[start_idx:start_idx + n_elem].reshape(params[key].shape)
start_idx += n_elem
return params_
def filter_policy_params(params):
Include only variables with "/pi/" (policy) or "/shared" (shared layers)
in their name: Only these ones affect the action.
:param params: (OrderedDict)
:return: (OrderedDict)
return OrderedDict((key, value) for key, value in params.items()
if ("/pi/" in key or "/shared" in key))
def evaluate(env, model):
Return mean fitness (negative sum of episodic rewards)
for given model.
:param env: (gym.Env)
:param model: (RL Model)
:return: (float)
episode_rewards = []
for _ in range(10):
reward_sum = 0
done = False
obs = env.reset()
while not done:
action, _states = model.predict(obs)
obs, reward, done, info = env.step(action)
reward_sum += reward
return - np.mean(episode_rewards)
# Create RL model with a small policy network
model = A2C('MlpPolicy', 'CartPole-v1', ent_coef=0.0, learning_rate=0.1,
policy_kwargs={'net_arch': [8]}, verbose=0)
# Use RL actor-critic policy gradient updates to
# find good initial parameters
# Get the parameters as the starting point for ES
model_params = model.get_parameters()
# Get only the policy parameters
policy_params = filter_policy_params(model_params)
# Create the Evolution Strategy (ES) object
es = cma.CMAEvolutionStrategy(flatten(policy_params), sigma0=1)
for iteration in range(10):
# Create population of candidates and evaluate them
candidates, fitnesses = es.ask(), []
for candidate in candidates:
# Load new policy parameters to agent.
model.load_parameters(to_dict(candidate, policy_params), exact_match=False)
# Evaluate the agent using stable-baselines predict function
fitnesses.append(evaluate(model.get_env(), model))
# CMA-ES update
es.tell(candidates, fitnesses)
# Display some training infos
mean_fitness = np.mean(sorted(fitnesses)[:int(0.1 * len(candidates))])
print("Iteration {:<3} Mean top 10% reward: {:.2f}".format(iteration, -mean_fitness))
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