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main.py
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main.py
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import numpy as np
import gym
from environment import RandomizedEnvironment
from agent import Agent
from replay_buffer import Episode, ReplayBuffer
EPISODES = 1000
experiment = "FetchReach-v1"
env = gym.make(experiment)
# Program hyperparameters
TESTING_INTERVAL = 50 # number of updates between two evaluation of the policy
TESTING_ROLLOUTS = 100 # number of rollouts performed to evaluate the current policy
# Algorithm hyperparameters
BATCH_SIZE = 32
BUFFER_SIZE = 100000
MAX_STEPS = 50 # WARNING: defined in multiple files...
GAMMA = 0.99
# Initialize the agent, both the actor/critic (and target counterparts) networks
agent = Agent(experiment, BATCH_SIZE*MAX_STEPS)
# Initialize the environment sampler
randomized_environment = RandomizedEnvironment(experiment, [], [])
# Initialize the replay buffer
replay_buffer = ReplayBuffer(BUFFER_SIZE)
# should be done per episode
for ep in range(EPISODES):
# generate a rollout
# generate an environment
randomized_environment.sample_env()
env, env_params = randomized_environment.get_env()
# reset the environment
current_obs_dict = env.reset()
# read the current goal, and initialize the episode
goal = current_obs_dict['desired_goal']
episode = Episode(goal, env_params, MAX_STEPS)
# get the first observation and first fake "old-action"
# TODO: decide if this fake action should be zero or random
obs = current_obs_dict['observation']
last_action = env.action_space.sample()
episode.add_step(last_action, obs, 0)
done = False
total_reward = 0
# rollout the whole episode
while not done:
obs = current_obs_dict['observation']
history = episode.get_history()
noise = agent.action_noise()
action = agent.evaluate_actor(agent._actor.predict, obs, goal, history) + noise
new_obs_dict, step_reward, done, info = env.step(action[0])
new_obs = new_obs_dict['observation']
episode.add_step(action[0], new_obs, step_reward)
total_reward += step_reward
current_obs_dict = new_obs_dict
# store the episode in the replay buffer
replay_buffer.add(episode)
# TODO: add HER with some probability to deal with sparse reward
# perform a batch update of the network if we can sample a big enough batch
# from the replay buffer
if replay_buffer.size() > BATCH_SIZE:
episodes = replay_buffer.sample_batch(BATCH_SIZE)
s_batch = np.zeros([BATCH_SIZE*MAX_STEPS, agent.get_dim_state()])
a_batch = np.zeros([BATCH_SIZE*MAX_STEPS, agent.get_dim_action()])
next_s_batch = np.zeros([BATCH_SIZE*MAX_STEPS, agent.get_dim_state()])
r_batch = np.zeros([BATCH_SIZE*MAX_STEPS])
env_batch = np.zeros([BATCH_SIZE*MAX_STEPS, agent.get_dim_env()])
goal_batch = np.zeros([BATCH_SIZE*MAX_STEPS, agent.get_dim_goal()])
history_batch = np.zeros([BATCH_SIZE*MAX_STEPS, MAX_STEPS, agent.get_dim_action()+agent.get_dim_state()])
t_batch = []
for i in range(BATCH_SIZE):
s_batch[i*MAX_STEPS:(i+1)*MAX_STEPS] = np.array(episodes[i].get_states())[:-1]
a_batch[i*MAX_STEPS:(i+1)*MAX_STEPS] = np.array(episodes[i].get_actions())[1:]
next_s_batch[i*MAX_STEPS:(i+1)*MAX_STEPS] = np.array(episodes[i].get_states())[1:]
r_batch[i*MAX_STEPS:(i+1)*MAX_STEPS] = np.array(episodes[i].get_rewards())[1:]
env_batch[i*MAX_STEPS:(i+1)*MAX_STEPS]=np.array(MAX_STEPS*[episodes[i].get_env()])
goal_batch[i*MAX_STEPS:(i+1)*MAX_STEPS]=np.array(MAX_STEPS*[episodes[i].get_goal()])
history_batch[i*MAX_STEPS:(i+1)*MAX_STEPS] = np.array([episodes[i].get_history(t = t) for t in range(1, MAX_STEPS+1)])
# WARNING FIXME: needs padding
t_batch += episodes[i].get_terminal()[1:]
target_action_batch = agent.evaluate_actor_batch(agent._actor.predict_target, next_s_batch, goal_batch, history_batch)
predicted_actions = agent.evaluate_actor_batch(agent._actor.predict, next_s_batch, goal_batch, history_batch)
target_q = agent.evaluate_critic_batch(agent._critic.predict_target, next_s_batch, predicted_actions, goal_batch, history_batch, env_batch)
y_i = []
for k in range(BATCH_SIZE*MAX_STEPS):
if t_batch[k]:
y_i.append(r_batch[k])
else:
y_i.append(r_batch[k] + GAMMA * target_q[k])
predicted_q_value, _ = agent.train_critic(s_batch, a_batch, goal_batch, history_batch, env_batch, np.reshape(y_i, (BATCH_SIZE*MAX_STEPS, 1)))
# Update the actor policy using the sampled gradient
a_outs = agent.evaluate_actor_batch(agent._actor.predict, s_batch, goal_batch, history_batch)
grads = agent.action_gradients_critic(s_batch, a_outs, goal_batch, history_batch, env_batch)
agent.train_actor(s_batch, goal_batch, history_batch, grads[0])
# Update target networks
agent.update_target_actor()
agent.update_target_critic()
randomized_environment.close_env()
# perform policy evaluation
if ep % TESTING_INTERVAL == 0:
success_number = 0
for test_ep in range(TESTING_ROLLOUTS):
randomized_environment.sample_env()
env, env_params = randomized_environment.get_env()
current_obs_dict = env.reset()
# read the current goal, and initialize the episode
goal = current_obs_dict['desired_goal']
episode = Episode(goal, env_params, MAX_STEPS)
# get the first observation and first fake "old-action"
# TODO: decide if this fake action should be zero or random
obs = current_obs_dict['observation']
last_action = env.action_space.sample()
episode.add_step(last_action, obs, 0)
done = False
# rollout the whole episode
while not done:
obs = current_obs_dict['observation']
history = episode.get_history()
action = agent.evaluate_actor(agent._actor.predict_target, obs, goal, history)
new_obs_dict, step_reward, done, info = env.step(action[0])
new_obs = new_obs_dict['observation']
episode.add_step(action[0], new_obs, step_reward)
total_reward += step_reward
current_obs_dict = new_obs_dict
if info['is_success'] > 0.0:
success_number += 1
randomized_environment.close_env()
print("Testing at episode {}, success rate : {}".format(ep, success_number/TESTING_ROLLOUTS))