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run_goal_paddpg.py
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run_goal_paddpg.py
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import os
import click
import time
import numpy as np
import gym
import gym_goal
from gym_goal.envs.config import GOAL_WIDTH, PITCH_LENGTH, PITCH_WIDTH
from gym.wrappers import Monitor
from agents.paddpg import PADDPGAgent
from common import ClickPythonLiteralOption
from common.wrappers import ScaledParameterisedActionWrapper
from common.goal_domain import GoalFlattenedActionWrapper, GoalObservationWrapper
from common.wrappers import ScaledStateWrapper
def pad_action(act, act_param):
params = [np.zeros((2,)), np.zeros((1,)), np.zeros((1,))]
params[act] = act_param
return (act, params)
def evaluate(env, agent, episodes=1000):
returns = []
timesteps = []
for _ in range(episodes):
state, _ = env.reset()
terminal = False
t = 0
total_reward = 0.
while not terminal:
t += 1
state = np.array(state, dtype=np.float32, copy=False)
act, act_param, _, _ = agent.act(state)
action = pad_action(act, act_param)
(state, _), reward, terminal, _ = env.step(action)
total_reward += reward
timesteps.append(t)
returns.append(total_reward)
return np.array(returns)
@click.command()
@click.option('--seed', default=0, help='Random seed.', type=int)
@click.option('--episodes', default=20000, help='Number of epsiodes.', type=int)
@click.option('--evaluation-episodes', default=1000, help='Episodes over which to evaluate after training.', type=int)
@click.option('--batch-size', default=128, help='Minibatch size.', type=int)
@click.option('--gamma', default=0.95, help='Discount factor.', type=float)
@click.option('--inverting-gradients', default=True,
help='Use inverting gradients scheme instead of squashing function.', type=bool)
@click.option('--initial-memory-threshold', default=128, help='Number of transitions required to start learning.',
type=int)
@click.option('--use-ornstein-noise', default=True,
help='Use Ornstein noise instead of epsilon-greedy with uniform random exploration.', type=bool)
@click.option('--replay-memory-size', default=20000, help='Replay memory size in transitions.', type=int)
@click.option('--epsilon-steps', default=1000, help='Number of episodes over which to linearly anneal epsilon.', type=int)
@click.option('--epsilon-final', default=0.01, help='Final epsilon value.', type=float)
@click.option('--tau-critic', default=0.01, help='Soft target network update averaging factor.', type=float)
@click.option('--tau-actor', default=0.0001, help='Soft target network update averaging factor.', type=float)
@click.option('--learning-rate-critic', default=1e-6, help="Critic network learning rate.", type=float)
@click.option('--learning-rate-actor', default=1e-7, help="Actor network learning rate.", type=float)
@click.option('--scale-actions', default=True, help="Scale actions.", type=bool)
@click.option('--reward-scale', default=1. / 50., help="Reward scaling factor.", type=float)
@click.option('--clip-grad', default=1., help="Parameter gradient clipping limit.", type=float)
@click.option('--initialise-params', default=True, help='Initialise action parameters.', type=bool)
@click.option('--save-dir', default="results/goal", help='Model save directory.', type=str)
@click.option('--layers', default="(128,64,)", help='Duplicate action-parameter inputs.', cls=ClickPythonLiteralOption)
@click.option('--title', default="PADDPG", help="Prefix of output files", type=str)
def run(seed, episodes, evaluation_episodes, batch_size, gamma, inverting_gradients, initial_memory_threshold,
replay_memory_size, scale_actions, epsilon_steps, epsilon_final, tau_actor, tau_critic, use_ornstein_noise,
learning_rate_actor, learning_rate_critic, reward_scale, clip_grad, initialise_params, layers, save_dir,
title):
env = gym.make('Goal-v0')
env = GoalObservationWrapper(env)
if scale_actions:
kickto_weights = np.array([[-0.375, 0.5, 0, 0.0625, 0],
[0, 0, 0.8333333333333333333, 0, 0.111111111111111111111111]])
shoot_goal_left_weights = np.array([0.857346647646219686, 0])
shoot_goal_right_weights = np.array([-0.857346647646219686, 0])
else:
xfear = 50.0 / PITCH_LENGTH
yfear = 50.0 / PITCH_WIDTH
caution = 5.0 / PITCH_WIDTH
kickto_weights = np.array([[2.5, 1, 0, xfear, 0], [0, 0, 1 - caution, 0, yfear]])
shoot_goal_left_weights = np.array([GOAL_WIDTH / 2 - 1, 0])
shoot_goal_right_weights = np.array([-GOAL_WIDTH / 2 + 1, 0])
initial_weights = np.zeros((4, 17))
initial_weights[0, [10, 11, 14, 15]] = kickto_weights[0, 1:]
initial_weights[1, [10, 11, 14, 15]] = kickto_weights[1, 1:]
initial_weights[2, 16] = shoot_goal_left_weights[1]
initial_weights[3, 16] = shoot_goal_right_weights[1]
initial_bias = np.zeros((4,))
initial_bias[0] = kickto_weights[0, 0]
initial_bias[1] = kickto_weights[1, 0]
initial_bias[2] = shoot_goal_left_weights[0]
initial_bias[3] = shoot_goal_right_weights[0]
env = GoalFlattenedActionWrapper(env)
if scale_actions:
env = ScaledParameterisedActionWrapper(env)
env = ScaledStateWrapper(env)
dir = os.path.join(save_dir, title)
env = Monitor(env, directory=os.path.join(dir, str(seed)), video_callable=False, write_upon_reset=False, force=True)
print(env.action_space)
print(env.observation_space)
env.seed(seed)
np.random.seed(seed)
agent = PADDPGAgent(observation_space=env.observation_space.spaces[0],
action_space=env.action_space,
batch_size=batch_size,
learning_rate_actor=learning_rate_actor,
learning_rate_critic=learning_rate_critic,
epsilon_steps=epsilon_steps,
epsilon_final=epsilon_final,
gamma=gamma,
clip_grad=clip_grad,
tau_actor=tau_actor,
tau_critic=tau_critic,
initial_memory_threshold=initial_memory_threshold,
use_ornstein_noise=use_ornstein_noise,
replay_memory_size=replay_memory_size,
inverting_gradients=inverting_gradients,
n_step_returns=False,
adam_betas=(0.9, 0.999),
critic_kwargs={'hidden_layers': layers, 'init_type': "kaiming"},
actor_kwargs={'hidden_layers': layers, 'init_type': "kaiming", # 'init_std': 1e-5, # 0.0001,
'squashing_function': False},
seed=seed)
if initialise_params:
agent.set_action_parameter_passthrough_weights(initial_weights, initial_bias)
print(agent)
max_steps = 150
total_reward = 0.
returns = []
start_time = time.time()
for i in range(episodes):
state, _ = env.reset()
state = np.array(state, dtype=np.float32, copy=False)
act, act_param, all_actions, all_action_parameters = agent.act(state)
action = pad_action(act, act_param)
episode_reward = 0.
agent.start_episode()
for j in range(max_steps):
ret = env.step(action)
(next_state, steps), reward, terminal, _ = ret
next_state = np.array(next_state, dtype=np.float32, copy=False)
next_act, next_act_param, next_all_actions, next_all_action_parameters = agent.act(next_state)
next_action = pad_action(next_act, next_act_param)
r = reward * reward_scale
agent.step(state, (act, act_param, all_actions, all_action_parameters), r, next_state,
(next_act, next_act_param, next_all_actions, next_all_action_parameters), terminal,
optimise=True)
act, act_param, all_actions, all_action_parameters = next_act, next_act_param, next_all_actions, next_all_action_parameters
action = next_action
state = next_state
episode_reward += reward
if terminal:
break
agent.end_episode()
returns.append(episode_reward)
total_reward += episode_reward
if (i + 1) % 100 == 0:
print('{0:5s} R:{1:.5f} P(S):{2:.4f}'.format(str(i + 1), total_reward / (i + 1),
(np.array(returns) == 50.).sum() / len(returns)))
end_time = time.time()
print("Took %.2f seconds" % (end_time - start_time))
env.close()
print(agent)
returns = env.get_episode_rewards()
np.save(os.path.join(dir, title + "{}".format(str(seed))), returns)
if evaluation_episodes > 0:
print("Evaluating agent over {} episodes".format(evaluation_episodes))
agent.epsilon_final = 0.
agent.epsilon = 0.
agent.noise = None
evaluation_returns = evaluate(env, agent, evaluation_episodes)
print("Ave. evaluation return =", sum(evaluation_returns) / len(evaluation_returns))
print("Ave. evaluation prob. =", sum(evaluation_returns == 50.) / len(evaluation_returns))
np.save(os.path.join(dir, title + "{}e".format(str(seed))), evaluation_returns)
if __name__ == '__main__':
run()