with open(path.join(outdir, fname), 'w') as fh:
fh.write(s)
info = {}
info['params'] = params
info['argv'] = sys.argv
info['env_id'] = env.spec.id
# ------------------------------------------
def noisy_evaluation(theta):
agent = BinaryActionLinearPolicy(theta)
rew, T = do_rollout(agent, env, num_steps)
return rew
# Train the agent, and snapshot each stage
for (i, iterdata) in enumerate(
cem(noisy_evaluation, np.zeros(env.observation_space.shape[0] + 1),
**params)):
print('Iteration %2i. Episode mean reward: %7.3f' %
(i, iterdata['y_mean']))
agent = BinaryActionLinearPolicy(iterdata['theta_mean'])
if args.display: do_rollout(agent, env, 200, render=True)
writefile('agent-%.4i.pkl' % i, str(pickle.dumps(agent, -1)))
# Write out the env at the end so we store the parameters of this
# environment.
writefile('info.json', json.dumps(info))
env.close()
def noisy_evaluation(theta):
agent = BinaryActionLinearPolicy(theta)
rew, T = do_rollout(agent, env, num_steps)
return rew
def writefile(fname, s):
with open(path.join(outdir, fname), "w") as fh:
fh.write(s)
info = {}
info["params"] = params
info["argv"] = sys.argv
info["env_id"] = env.spec.id
# ------------------------------------------
def noisy_evaluation(theta):
agent = BinaryActionLinearPolicy(theta)
rew, T = do_rollout(agent, env, num_steps)
return rew
# Train the agent, and snapshot each stage
for (i, iterdata) in enumerate(
cem(noisy_evaluation, np.zeros(env.observation_space.shape[0] + 1), **params)
):
print("Iteration %2i. Episode mean reward: %7.3f" % (i, iterdata["y_mean"]))
agent = BinaryActionLinearPolicy(iterdata["theta_mean"])
if args.display:
do_rollout(agent, env, 200, render=True)
writefile("agent-%.4i.pkl" % i, str(pickle.dumps(agent, -1)))
# Write out the env at the end so we store the parameters of this
# environment.
writefile("info.json", json.dumps(info))
env.close()