def __init__(self,
agent,
target_agent,
args,
count_model=None,
buffer=None):
self.args = args
self.agent = agent
self.target_agent = target_agent
# self.parameters = self.agent.parameters()
self.agent_parameters = list(self.agent.parameters())
if args.atari_rms:
self.optimiser = torch.optim.RMSprop(params=self.agent_parameters,
lr=self.args.lr,
alpha=0.95,
eps=0.00001,
centered=True)
else:
self.optimiser = torch.optim.RMSprop(params=self.agent_parameters,
lr=self.args.lr)
self.logger = logging.getLogger("DQNTrainer")
self.stats = get_stats()
self.count_model = count_model
self.nstep = args.n_step > 1
self.goal_samples = 0
self.buffer = buffer
def __init__(self, config, device):
self.config = config
self.args = config # V. Lazy coding
num_actions = config.num_actions
if not self.config.count_state_action:
raise Exception
self.num_actions = num_actions
self.net = dora_specifier(config.dora_name)(config).to(device)
self.target_net = dora_specifier(config.dora_name)(config).to(device)
self.target_net.load_state_dict(self.net.state_dict())
self.stats = get_stats()
self.reward_directly = True
self.train_times = config.batch_size
self.states = None
self.states_idx = 0
self.device = device
self.agent_parameters = self.net.parameters()
self.optimiser = torch.optim.RMSprop(params=self.agent_parameters,
lr=self.config.lr)
def __init__(self, args):
self.epsilon_start = args.epsilon_start
self.epsilon_finish = args.epsilon_finish
self.epsilon_time_length = args.epsilon_time_length
self.num_actions = args.num_actions
self.stats = get_stats()
self.logger = logging.getLogger("EpsGreedy")
def __init__(self, config):
# self.hash_counts = [HashingBonusEvaluator(dim_key=config.count_size, obs_processed_flat_dim=np.prod(config.state_shape)) for _ in range(config.num_actions)]
self.hash_counts = HashingBonusEvaluator(dim_key=config.count_size, obs_processed_flat_dim=np.prod(config.state_shape), actions=config.num_actions)
self.hash_state_count = HashingBonusEvaluator(dim_key=config.count_size, obs_processed_flat_dim=np.prod(config.state_shape), actions=1)
self.config = config
self.flatten = True if len(config.state_shape) > 1 else False
self.stats = get_stats()
def __init__(self, args):
self.epsilon_start = args.epsilon_start
self.epsilon_finish = args.epsilon_finish
self.epsilon_time_length = args.epsilon_time_length
self.num_actions = args.num_actions
self.stats = get_stats()
self.logger = logging.getLogger("BSPAction")
self.config = args
self.current_k = 0
def __init__(self, count_model, args):
self.epsilon_start = args.epsilon_start
self.epsilon_finish = args.epsilon_finish
self.epsilon_time_length = args.epsilon_time_length
self.num_actions = args.num_actions
self.stats = get_stats()
self.logger = logging.getLogger("OptimisticAction")
self.count_model = count_model
self.m = args.optim_m
self.tau = args.optim_action_tau
self.config = args
def __init__(self, config):
self.config = config
self.num_actions = self.config.num_actions
self.state_action_counters = [
defaultdict(lambda: 0) for _ in range(self.num_actions)
]
self.target_shape = (config.atari_target_x, config.atari_target_y)
self.max_pix_value = 255
self.stats = get_stats()
self.hash_vector = None
def __init__(self, count_model, args):
self.epsilon_start = args.epsilon_start
self.epsilon_finish = args.epsilon_finish
self.epsilon_time_length = args.epsilon_time_length
self.num_actions = args.num_actions
self.stats = get_stats()
self.logger = logging.getLogger("CountBonus")
self.min_q = 1000
self.max_q = -1000
self.count_model = count_model
self.args = args
def __init__(self, game):
self.mont_env = gym.make("{}NoFrameskip-v4".format(game))
# self.mario_env = modewrapper(self.mario_env)
self.max_timesteps = 4500
self.mont_env = make_atari(self.mont_env, max_episode_steps=self.max_timesteps)
self.steps = 0
self._seed = 56
self.observation_space = self.mont_env.observation_space
self.action_space = self.mont_env.action_space
self.stats = get_stats()
self.obs_dtype = np.uint8
self.obs_scaling = 1/255.0
def __init__(self):
self.mont_env = gym.make("MontezumaRevengeNoFrameskip-v4")
# self.mario_env = modewrapper(self.mario_env)
self.max_timesteps = 4500
self.mont_env = make_montezuma(self.mont_env, max_episode_steps=self.max_timesteps)
self.steps = 0
self._seed = 56
self.observation_space = self.mont_env.observation_space
self.action_space = self.mont_env.action_space
self.stats = get_stats()
self.obs_dtype = np.uint8
self.obs_scaling = 1/255.0
def __init__(self, config, device):
self.config = config
# assert self.config.count_state_action # For now
num_actions = config.num_actions
if not self.config.count_state_action:
num_actions = 1
self.num_actions = num_actions
# Maybe the predictors should all start the same and have the same target
self.predictors = [rnd_specifier.get_pred(config.rnd_net_name)(config).to(device) for _ in range(num_actions)]
self.targets = [rnd_specifier.get_target(config.rnd_net_name)(config).to(device) for _ in range(num_actions)]
self.states = [None for _ in range(num_actions)]
self.states_idx = [0 for _ in range(num_actions)]
if self.config.rnd_same_starts:
p_dict = self.predictors[0].state_dict()
t_dict = self.targets[0].state_dict()
for p in self.predictors:
p.load_state_dict(p_dict)
for t in self.targets:
t.load_state_dict(t_dict)
if self.config.count_state_only_rewards:
self.state_predictor = rnd_specifier.get_pred(config.rnd_net_name)(config).to(device)
self.state_target = rnd_specifier.get_target(config.rnd_net_name)(config).to(device)
if self.config.rnd_same_starts:
self.state_predictor.load_state_dict(p_dict)
self.state_target.load_state_dict(t_dict)
self.state_states = None
self.state_states_idx = 0
self.stats = get_stats()
self.reward_directly = True
# Training stuff
self.net_parameters = []
for n in self.predictors:
self.net_parameters += n.parameters()
if self.config.count_state_only_rewards:
self.net_parameters += self.state_predictor.parameters()
self.optimiser = torch.optim.RMSprop(params=self.net_parameters, lr=self.config.lr)
self.train_times = self.config.rnd_train_times if self.config.rnd_train_times > 0 else self.config.rnd_batch_size
def main(_config, _run):
config = convert(_config)
_id = _run._id
# Logging stuff
logger = logging.getLogger("Main")
if config.mongo:
logging.disable(logging.WARNING)
configure_stats_logging(
str(_id) + "_" + config.name,
log_interval=config.log_interval,
sacred_info=_run.info,
use_tb=config.tb,
)
stats = get_stats()
logger.critical("ID: {}".format(_id))
# Update config with environment specific information
env = gym.make(config.env)
num_actions = env.action_space.n
config = config._replace(num_actions=num_actions)
state_shape = env.observation_space.shape
config = config._replace(state_shape=state_shape)
# Wrap env
env = EnvWrapper(env, debug=True, args=config)
# Log the config
config_str = "Config:\n\n"
for k, v in sorted(config._asdict().items()):
config_str += " {}: {}\n".format(k, v)
logger.critical(config_str)
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
logger.critical("Device: {}".format(device.type))
# Make agent and target agent
agent = get_model(config.agent)(config)
target_agent = get_model(config.agent)(config)
target_agent.load_state_dict(agent.state_dict())
agent.to(device)
target_agent.to(device)
# Pseudocount stuff
count_model = None
if config.count_rewards:
if config.atari_count:
count_model = AtariCount(config)
elif config.rnd_net_count:
# assert config.count_state_only_rewards
count_model = RndNetworkDistill(config, device)
elif config.dora_count:
count_model = DoraCount(config, device)
else:
count_model = PseudoCount(config)
# Make action selector
action_selector = None
if config.action_selector == "eps_greedy":
action_selector = eps_greedy.EpsGreedy(config)
elif config.action_selector == "optimistic_action":
action_selector = optimistic_action.OptimisticAction(
count_model, config)
elif config.action_selector == "bsp":
action_selector = bsp_action.BSPAction(config)
else:
raise Exception("{} is not an Action Selector!".format(
config.action_selector))
# Make replay buffer
# Check if the obs dtype of the environment is an int
obs_dtype = getattr(env.wrapped_env, "obs_dtype", np.float32)
obs_scaling = getattr(env.wrapped_env, "obs_scaling", 1)
replay_buffer = ReplayBuffer(size=config.buffer_size,
frame_history_len=config.past_frames_input,
obs_dtype=obs_dtype,
obs_scaling=obs_scaling,
args=config)
if config.dora_count:
dora_buffer = ReplayBuffer(size=config.batch_size * 4,
frame_history_len=config.past_frames_input,
obs_dtype=obs_dtype,
obs_scaling=obs_scaling,
args=config)
# Make trainer
trainer = None
if config.trainer == "DQN":
trainer = DQNTrainer(agent=agent,
target_agent=target_agent,
args=config,
count_model=count_model,
buffer=replay_buffer)
else:
raise Exception
testing_buffer = ReplayBuffer(size=(config.past_frames_input + 1),
frame_history_len=config.past_frames_input,
args=config)
# Testing stuff
testing_env = EnvWrapper(env=gym.make(config.env), debug=True, args=config)
if config.test_augmented:
assert config.action_selector == "optimistic_action"
# Player Positions
positions = set()
action_positions = set()
T = 0
start_time = time.time()
last_time = start_time
# Lots of code duplication :(
logging.critical("Filling buffer with {:,} random experiences.".format(
config.buffer_burn_in))
state = env.reset()
assert config.buffer_burn_in == 0
for t in range(config.buffer_burn_in):
buffer_idx = replay_buffer.store_frame(state)
stacked_states = replay_buffer.encode_recent_observation()
tensor_state = torch.tensor(stacked_states, device=device).unsqueeze(0)
action = np.random.randint(config.num_actions)
next_state, reward, terminated, info = env.step(action)
terminal_to_store = terminated
if "Steps_Termination" in info and info["Steps_Termination"]:
terminal_to_store = False
intrinsic_reward = 0
pseudo_count = 0
if config.count_rewards:
pseudo_count = count_model.visit(tensor_state, action)
if getattr(count_model, "reward_directly", False):
intrinsic_reward = pseudo_count
else:
count_bonus = config.count_beta / sqrt(pseudo_count)
intrinsic_reward = count_bonus
replay_buffer.store_effect(buffer_idx, action,
reward - config.reward_baseline,
intrinsic_reward, terminal_to_store,
pseudo_count)
state = next_state
if terminated:
state = env.reset()
logger.warning("Random action burn in t: {:,}".format(t))
state = env.reset()
episode = 0
episode_reward = 0
intrinsic_episode_reward = 0
episode_length = 0
env_positive_reward = 0
max_episode_reward = 0
if config.bsp:
bsp_k = np.random.randint(config.bsp_k)
action_selector.update_k(bsp_k)
logging.critical("Beginning training.")
while T < config.t_max:
# Store the current state
buffer_idx = replay_buffer.store_frame(state)
if config.dora_count:
dora_idx = dora_buffer.store_frame(state)
# Get the stacked input vector
stacked_states = replay_buffer.encode_recent_observation()
# Get output from agent
with torch.no_grad():
tensor_state = torch.tensor(stacked_states,
device=device).unsqueeze(0)
agent_output = agent(tensor_state)
# agent_output = agent(torch.Tensor(stacked_states).unsqueeze(0))
# Select action
action, action_info = action_selector.select_actions(
agent_output, T, info={"state": tensor_state})
# Take an environment step
next_state, reward, terminated, info = env.step(action)
T += 1
stats.update_t(T)
episode_reward += reward
episode_length += 1
terminal_to_store = terminated
if "Steps_Termination" in info and info["Steps_Termination"]:
logger.warning("Terminating because of episode limit")
terminal_to_store = False
# Log if a positive reward was ever received from environment. ~Finding goal
if reward > 0.1:
env_positive_reward = 1
stats.update_stats("Positive_Reward", env_positive_reward)
# Calculate count based intrinsic motivation
intrinsic_reward = 0
pseudo_count = 0
if config.count_rewards:
pseudo_count = count_model.visit(tensor_state, action)
if getattr(count_model, "reward_directly", False):
# The count-model is giving us the intrinsic reward directly
intrinsic_reward = pseudo_count[0]
else:
# Count-model is giving us the pseudo-count
count_bonus = config.count_beta / sqrt(pseudo_count)
intrinsic_reward = count_bonus
intrinsic_episode_reward += intrinsic_reward
# Render training
if config.render_train_env:
debug_info = {}
debug_info.update(action_info)
env.render(debug_info=debug_info)
# Add what happened to the buffer
replay_buffer.store_effect(buffer_idx, action,
reward - config.reward_baseline,
intrinsic_reward, terminal_to_store,
pseudo_count)
if config.dora_count:
dora_buffer.store_effect(dora_idx, action,
reward - config.reward_baseline,
intrinsic_reward, terminal_to_store,
pseudo_count)
# Update state
state = next_state
# If terminated
if terminated:
# If we terminated due to episode limit, we need to add the current state in
if "Steps_Termination" in info and info["Steps_Termination"]:
buffer_idx = replay_buffer.store_frame(state)
replay_buffer.store_effect(buffer_idx,
0,
0,
0,
True,
0,
dont_sample=True)
if config.dora_count:
dora_idx = dora_buffer.store_frame(state)
dora_buffer.store_effect(dora_idx,
0,
0,
0,
True,
0,
dont_sample=True)
logger.warning("T: {:,}, Episode Reward: {:.2f}".format(
T, episode_reward))
state = env.reset()
max_episode_reward = max(max_episode_reward, episode_reward)
stats.update_stats("Episode Reward", episode_reward)
stats.update_stats("Max Episode Reward", max_episode_reward)
stats.update_stats("Episode Length", episode_length)
stats.update_stats("Intrin Eps Reward", intrinsic_episode_reward)
episode_reward = 0
episode_length = 0
intrinsic_episode_reward = 0
episode += 1
stats.update_stats("Episode", episode)
if config.bsp:
bsp_k = np.random.randint(config.bsp_k)
action_selector.update_k(bsp_k)
# Train if possible
for _ in range(config.training_iters):
sampled_batch = None
if T % config.update_freq != 0:
# Only train every update_freq timesteps
continue
if replay_buffer.can_sample(config.batch_size):
sampled_batch = replay_buffer.sample(config.batch_size,
nstep=config.n_step)
if sampled_batch is not None:
trainer.train(sampled_batch)
if config.dora_count:
if dora_buffer.can_sample(config.batch_size):
sampled_batch = replay_buffer.sample(config.batch_size,
nstep=config.n_step)
if sampled_batch is not None:
count_model.train(sampled_batch)
# Update target networks if necessary
if T % config.target_update_interval == 0:
trainer.update_target_agent()
if config.dora_count:
count_model.update_target_agent()
# Logging
if config.bsp:
agent_output = agent_output[:, :, bsp_k]
q_vals_numpy = agent_output.detach().cpu()[0].numpy()
if num_actions < 20:
for action_id in range(config.num_actions):
stats.update_stats("Q-Value_{}".format(action_id),
q_vals_numpy[action_id])
else:
stats.update_stats("Q-Value_Mean", np.mean(q_vals_numpy))
player_pos = env.log_visitation()
positions.add(player_pos)
action_positions.add((player_pos, action))
stats.update_stats("States Visited", len(positions))
stats.update_stats("State_Actions Visited", len(action_positions))
stats.update_stats("Player Position", player_pos)
# Log all env stats returned
for k, v in info.items():
if k != "Steps_Termination":
stats.update_stats(k, v)
if config.save_count_gifs > 0 and T % config.save_count_gifs == 0:
if count_model is not None:
state_action_counts, count_nums = env.count_state_action_space(
count_model)
if state_action_counts is not None:
save_image(state_action_counts,
image_name="SA_Counts__{}_Size__{}_T".format(
config.count_size, T),
direc_name="State_Action_Counts")
save_sa_count_vals(count_nums,
name="SA_PCounts__{}_Size__{}_T".format(
config.count_size, T),
direc_name="Sa_Count_Estimates")
actual_counts = env.state_counts()
if actual_counts is not None:
save_actual_counts(actual_counts,
name="Counts__{}_T".format(T),
direc_name="Actual_Counts")
q_val_img, q_vals = env.q_value_estimates(count_model, agent)
if q_val_img is not None:
save_image(q_val_img,
image_name="Q_Vals__{}_Size__{}_T".format(
config.count_size, T),
direc_name="Q_Value_Estimates")
if q_vals is not None:
save_q_vals(q_vals,
name="Q_Vals__{}_Size__{}_T".format(
config.count_size, T),
direc_name="Q_Value_Estimates")
# Testing
with torch.no_grad():
if T % config.testing_interval == 0:
prefixes = [""]
if config.test_augmented:
prefixes += ["Aug_"]
for prefix in prefixes:
total_test_reward = 0
total_test_length = 0
for _ in range(config.test_episodes):
test_episode_reward = 0
test_episode_length = 0
test_state = testing_env.reset()
test_env_terminated = False
while not test_env_terminated:
test_buffer_idx = testing_buffer.store_frame(
test_state)
stacked_test_states = testing_buffer.encode_recent_observation(
)
test_tensor_state = torch.tensor(
stacked_test_states,
device=device).unsqueeze(0)
testing_agent_output = agent(test_tensor_state)
if prefix == "Aug_" or config.bsp:
test_action, _ = action_selector.select_actions(
testing_agent_output,
T,
info={"state": test_tensor_state},
testing=True)
else:
test_action = get_test_action(
testing_agent_output, config)
next_test_state, test_reward, test_env_terminated, _ = testing_env.step(
test_action)
if config.render_test_env:
testing_env.render()
test_episode_length += 1
test_episode_reward += test_reward
testing_buffer.store_effect(
test_buffer_idx, test_action, test_reward, 0,
test_env_terminated, 0)
test_state = next_test_state
total_test_length += test_episode_length
total_test_reward += test_episode_reward
mean_test_reward = total_test_reward / config.test_episodes
mean_test_length = total_test_length / config.test_episodes
logger.error(
"{}Testing -- T: {:,}/{:,}, Test Reward: {:.2f}, Test Length: {:,}"
.format(prefix, T, config.t_max, mean_test_reward,
mean_test_length))
stats.update_stats("{}Test Reward".format(prefix),
mean_test_reward,
always_log=True)
stats.update_stats("{}Test Episode Length".format(prefix),
mean_test_length,
always_log=True)
logger.error("Estimated time left: {}. Time passed: {}".format(
time_left(last_time, T - config.testing_interval,
T, config.t_max),
time_str(time.time() - start_time)))
last_time = time.time()
if T % (config.log_interval * 4) == 0:
stats.print_stats()
logger.critical("Closing envs")
env.close()
testing_env.close()
logger.critical("Finished training.")
if client is not None:
logger.critical("Attempting to close pymongo client")
client.close()
logger.critical("Pymongo client closed")
logger.critical("Exiting")
