def trainer(fargs):
trainer_id, args = fargs
print("Trainer id", trainer_id, "started")
# Create a Gym environment
env = gym.make(args.env)
# Set maximum episode length
if args.episode_steps is not None:
env._max_episode_steps = args.episode_steps
# Get dimensionalities of actions and observations
action_space_dim = get_space_dim(env.action_space)
observation_space_dim = get_space_dim(env.observation_space)
# Instantiate agent and its policy
policy = Policy(observation_space_dim, action_space_dim)
agent = Agent(policy)
training_history = train(agent,
env,
args.train_episodes,
silent=True,
train_run_id=trainer_id,
early_stop=False)
print("Trainer id", trainer_id, "finished")
return training_history
def main(args):
# Create a Gym environment
env = gym.make(args.env)
# Exercise 1
# TODO: For CartPole-v0 - maximum episode length
env._max_episode_steps = 1000
# Get dimensionalities of actions and observations
action_space_dim = get_space_dim(env.action_space)
observation_space_dim = get_space_dim(env.observation_space)
# Instantiate agent and its policy
policy = Policy(observation_space_dim, action_space_dim)
agent = Agent(policy)
# Print some stuff
print("Environment:", args.env)
print("Training device:", agent.train_device)
print("Observation space dimensions:", observation_space_dim)
print("Action space dimensions:", action_space_dim)
# If no model was passed, train a policy from scratch.
# Otherwise load the policy from the file and go directly to testing.
if args.test is None:
training_history = train(args.position, agent, env,
args.train_episodes, False,
args.render_training)
# Save the model
tt = str(datetime.datetime.now().date()) + "-" + str(
datetime.datetime.now().hour) + "-" + str(
datetime.datetime.now().minute)
model_file = "%s_params.mdl" % (args.env + tt + "vel")
torch.save(policy.state_dict(), model_file)
print("Model saved to", model_file)
# Plot rewards
sns.lineplot(x="episode", y="reward", data=training_history)
sns.lineplot(x="episode", y="mean_reward", data=training_history)
plt.legend(["Reward", "100-episode average"])
plt.title("Reward history (%s)" % args.env)
# time and day of plot
plt.savefig("train_history" + tt + "vel" + ".jpg")
plt.show()
print("Training finished.")
else:
print("Loading model from", args.test, "...")
state_dict = torch.load(args.test)
policy.load_state_dict(state_dict)
print("Testing...")
test(args.position, agent, env, args.train_episodes, args.render_test)
def main(args):
# Create a Gym environment
env = gym.make(args.env)
# Exercise 1
env._max_episode_steps = args.episode_length
# Get dimensionalities of actions and observations
action_space_dim = get_space_dim(env.action_space)
observation_space_dim = get_space_dim(env.observation_space)
# Instantiate agent and its policy
policy = Policy(observation_space_dim, action_space_dim)
agent = Agent(policy)
# Print some stuff
print("Environment:", args.env)
print("Training device:", agent.train_device)
print("Observation space dimensions:", observation_space_dim)
print("Action space dimensions:", action_space_dim)
# If no model was passed, train a policy from scratch.
# Otherwise load the policy from the file and go directly to testing.
if args.test is None:
training_history = train(agent,
env,
args.train_episodes,
False,
args.render_training,
x0=args.x0,
args=args,
policy=policy)
# Save the model
model_file = "%s_params.mdl" % args.env
torch.save(policy.state_dict(), model_file)
print("Model saved to", model_file)
# Plot rewards
sns.lineplot(x="episode", y="reward", data=training_history)
sns.lineplot(x="episode", y="mean_reward", data=training_history)
plt.legend(["Reward", "100-episode average"])
plt.title("Reward history (%s)" % args.env)
plt.show()
print("Training finished.")
else:
print("Loading model from", args.test, "...")
state_dict = torch.load(args.test)
policy.load_state_dict(state_dict)
print("Testing...")
test(agent, env, args.train_episodes, args.render_test, x0=args.x0)
# Let the agent do its magic (update the policy)
agent.episode_finished(episode_number)
# Training is finished - plot rewards
plt.plot(reward_history)
plt.plot(average_reward_history)
plt.legend(["Reward", "100-episode average"])
plt.title("Reward history")
plt.show()
print("Training finished.")
# Create a Gym environment
env = CartPoleEnv()
# For CartPole - maximum episode length
env._max_episode_steps = 1000
# Get dimensionalities of actions and observations
action_space_dim = get_space_dim(env.action_space)
observation_space_dim = get_space_dim(env.observation_space)
# Create the agent and the policy
policy = Policy(observation_space_dim, action_space_dim)
agent = Agent(policy)
# Train
train(args.train_episodes, agent)
model_file = "%s_params.mdl" % args.env
torch.save(policy.state_dict(), model_file)
print("Model saved to", model_file)
