def run(env_name='BanditOneHot2-v0',
num_episodes=1,
epsilon=0.1,
epsilon_decay_tau=0,
lr_R=.1,
master_seed=42,
write_to_disk=True,
load=None,
log_dir=None):
"""Play some slots!"""
# --- Init ---
writer = SummaryWriter(log_dir=log_dir, write_to_disk=write_to_disk)
# -
env = gym.make(env_name)
env.seed(master_seed)
num_actions = env.action_space.n
best_action = env.best
# -
default_reward_value = 0.0
R_t = default_reward_value
critic = Critic(num_actions, default_value=default_reward_value)
actor = EpsilonActor(num_actions,
epsilon=epsilon,
decay_tau=epsilon_decay_tau,
seed_value=master_seed)
all_actions = list(range(num_actions))
# Update with pre-loaded data. This will let you run
# test experiments on pre-trained model and/or to
# continue training.
if load is not None:
result = load_checkpoint(load)
critic.load_state_dict(result['critic'])
# -
num_best = 0
total_R = 0.0
total_regret = 0.0
# ------------------------------------------------------------------------
for n in range(num_episodes):
env.reset()
# Choose an action; Choose a bandit
action = actor(list(critic.model.values()))
if action in best_action:
num_best += 1
# Est. regret and save it
regret = estimate_regret(all_actions, action, critic)
# Pull a lever.
state, R_t, _, _ = env.step(action)
# Critic learns
critic = Q_update(action, R_t, critic, lr_R)
# Log data
writer.add_scalar("state", int(state), n)
writer.add_scalar("action", action, n)
writer.add_scalar("epsilon", actor.epsilon, n)
writer.add_scalar("regret", regret, n)
writer.add_scalar("score_R", R_t, n)
writer.add_scalar("value_R", critic(action), n)
total_R += R_t
total_regret += regret
writer.add_scalar("total_regret", total_regret, n)
writer.add_scalar("total_R", total_R, n)
writer.add_scalar("p_bests", num_best / (n + 1), n)
# Decay ep?
if epsilon_decay_tau > 0:
actor.decay_epsilon()
# -- Build the final result, and save or return it ---
writer.close()
result = dict(best=env.best,
env_name=env_name,
num_episodes=num_episodes,
lr_R=lr_R,
epsilon=epsilon,
epsilon_decay_tau=epsilon_decay_tau,
critic=critic.state_dict(),
total_R=total_R,
total_regret=total_regret,
master_seed=master_seed)
if write_to_disk:
save_checkpoint(result,
filename=os.path.join(writer.log_dir, "result.pkl"))
return result
def run(env_name='BanditOneHot10-v0',
num_episodes=1000,
tie_break='next',
tie_threshold=0.0,
lr_R=.1,
master_seed=42,
initial_bins=None,
write_to_disk=True,
load=None,
log_dir=None):
"""Bandit agent - argmax (E, R)"""
# --- Init ---
writer = SummaryWriter(log_dir=log_dir, write_to_disk=write_to_disk)
# -
env = gym.make(env_name)
env.seed(master_seed)
env.reset()
num_actions = env.action_space.n
all_actions = list(range(num_actions))
best_action = env.best
default_reward_value = 0
default_info_value = entropy(np.ones(num_actions) / num_actions)
E_t = default_info_value
R_t = default_reward_value
# - Init agents and memories
critic_R = Critic(num_actions, default_value=default_reward_value)
critic_E = Critic(num_actions, default_value=default_info_value)
actor_R = DeterministicActor(num_actions,
tie_break='first',
tie_threshold=tie_threshold)
actor_E = DeterministicActor(num_actions,
tie_break=tie_break,
tie_threshold=tie_threshold)
memories = [
DiscreteDistribution(initial_bins=initial_bins)
for _ in range(num_actions)
]
# Update with pre-loaded data. This will let you run
# test experiments on pre-trained model and/or to
# continue training.
if load is not None:
result = load_checkpoint(load)
critic_E.load_state_dict(result['critic_E'])
critic_R.load_state_dict(result['critic_R'])
for i, mem in enumerate(memories):
mem.load_state_dict(result['memories'][i])
# -
num_best = 0
total_R = 0.0
total_E = 0.0
total_regret = 0.0
# ------------------------------------------------------------------------
for n in range(num_episodes):
env.reset()
# Meta-greed policy selection
if (E_t - tie_threshold) > R_t:
critic = critic_E
actor = actor_E
policy = 0
else:
critic = critic_R
actor = actor_R
policy = 1
# Choose an action; Choose a bandit
action = actor(list(critic.model.values()))
if action in best_action:
num_best += 1
# Est. regret and save it
regret = estimate_regret(all_actions, action, critic)
# Pull a lever.
state, R_t, _, _ = env.step(action)
R_t = R_homeostasis(R_t, total_R, num_episodes)
# Estimate E
old = deepcopy(memories[action])
memories[action].update((int(state), int(R_t)))
new = deepcopy(memories[action])
E_t = kl(new, old, default_info_value)
# Learning, both policies.
critic_R = R_update(action, R_t, critic_R, lr_R)
critic_E = E_update(action, E_t, critic_E, lr=1)
# Log data
writer.add_scalar("policy", policy, n)
writer.add_scalar("state", state, n)
writer.add_scalar("action", action, n)
writer.add_scalar("regret", regret, n)
writer.add_scalar("score_E", E_t, n)
writer.add_scalar("score_R", R_t, n)
writer.add_scalar("value_E", critic_E(action), n)
writer.add_scalar("value_R", critic_R(action), n)
total_E += E_t
total_R += R_t
total_regret += regret
writer.add_scalar("total_regret", total_regret, n)
writer.add_scalar("total_E", total_E, n)
writer.add_scalar("total_R", total_R, n)
writer.add_scalar("p_bests", num_best / (n + 1), n)
tie = 0
if actor.tied:
tie = 1
writer.add_scalar("ties", tie, n)
# -- Build the final result, and save or return it ---
writer.close()
result = dict(best=env.best,
num_episodes=num_episodes,
tie_break=tie_break,
tie_threshold=tie_threshold,
critic_E=critic_E.state_dict(),
critic_R=critic_R.state_dict(),
memories=[m.state_dict() for m in memories],
total_E=total_E,
total_R=total_R,
total_regret=total_regret,
env_name=env_name,
lr_R=lr_R,
master_seed=master_seed)
if write_to_disk:
save_checkpoint(result,
filename=os.path.join(writer.log_dir, "result.pkl"))
return result
def run(env_name='BanditOneHot10-v0',
num_episodes=1000,
temp=1.0,
tie_threshold=0.0,
tie_break=None,
lr_R=.1,
master_seed=42,
write_to_disk=True,
log_dir=None):
"""Bandit agent - softmax (E, R)"""
# --- Init ---
writer = SummaryWriter(log_dir=log_dir, write_to_disk=write_to_disk)
# -
env = gym.make(env_name)
env.seed(master_seed)
num_actions = env.action_space.n
all_actions = list(range(num_actions))
best_action = env.best
default_reward_value = 0
default_info_value = entropy(np.ones(num_actions) / num_actions)
E_t = default_info_value
R_t = default_reward_value
# --- Agents and memories ---
critic_R = Critic(num_actions, default_value=default_reward_value)
critic_E = Critic(num_actions, default_value=default_info_value)
actor_R = SoftmaxActor(num_actions, temp=temp, seed_value=master_seed)
actor_E = SoftmaxActor(num_actions, temp=temp, seed_value=master_seed)
memories = [DiscreteDistribution() for _ in range(num_actions)]
# -
num_best = 0
total_R = 0.0
total_E = 0.0
total_regret = 0.0
# ------------------------------------------------------------------------
for n in range(num_episodes):
env.reset()
# Meta-greed policy selection
if (E_t - tie_threshold) > R_t:
critic = critic_E
actor = actor_E
policy = 0
else:
critic = critic_R
actor = actor_R
policy = 1
# Choose an action; Choose a bandit
action = actor(list(critic.model.values()))
if action in best_action:
num_best += 1
# Est. regret and save it
regret = estimate_regret(all_actions, action, critic)
# Pull a lever.
state, R_t, _, _ = env.step(action)
R_t = R_homeostasis(R_t, total_R, num_episodes)
# Estimate E
old = deepcopy(memories[action])
memories[action].update((int(state), int(R_t)))
new = deepcopy(memories[action])
E_t = kl(new, old, default_info_value)
# Learning, both policies.
critic_R = R_update(action, R_t, critic_R, lr_R)
critic_E = E_update(action, E_t, critic_E, lr=1)
# Log data
writer.add_scalar("policy", policy, n)
writer.add_scalar("state", int(state), n)
writer.add_scalar("action", action, n)
writer.add_scalar("regret", regret, n)
writer.add_scalar("score_E", E_t, n)
writer.add_scalar("score_R", R_t, n)
writer.add_scalar("value_E", critic_E(action), n)
writer.add_scalar("value_R", critic_R(action), n)
total_E += E_t
total_R += R_t
total_regret += regret
writer.add_scalar("total_regret", total_regret, n)
writer.add_scalar("total_E", total_E, n)
writer.add_scalar("total_R", total_R, n)
writer.add_scalar("p_bests", num_best / (n + 1), n)
# -- Build the final result, and save or return it ---
writer.close()
result = dict(best=env.best,
num_episodes=num_episodes,
temp=temp,
tie_threshold=tie_threshold,
critic_E=critic_E.state_dict(),
critic_R=critic_R.state_dict(),
total_E=total_E,
total_R=total_R,
total_regret=total_regret,
env_name=env_name,
lr_R=lr_R,
master_seed=master_seed)
if write_to_disk:
save_checkpoint(result,
filename=os.path.join(writer.log_dir, "result.pkl"))
return result
def run(env_name='BanditOneHigh2-v0',
num_episodes=1,
temp=1.0,
beta=1.0,
lr_R=.1,
mode='EB',
master_seed=42,
write_to_disk=True,
load=None,
log_dir=None):
"""Bandit agent - sample(R + beta count^(-1/2))"""
# --- Init ---
writer = SummaryWriter(log_dir=log_dir, write_to_disk=write_to_disk)
# -
env = gym.make(env_name)
env.seed(master_seed)
num_actions = env.action_space.n
all_actions = list(range(num_actions))
best_action = env.best
default_reward_value = 0 # Null R
R_t = default_reward_value
# Agents and memories
critic = Critic(num_actions, default_value=default_reward_value)
actor = SoftmaxActor(num_actions, temp=temp, seed_value=master_seed)
count = CountMemory()
# Update with pre-loaded data. This will let you run
# test experiments on pre-trained model and/or to
# continue training.
if load is not None:
result = load_checkpoint(load)
critic.load_state_dict(result['critic'])
count.load_state_dict(result['count'])
# -
total_R = 0.0
total_regret = 0.0
num_best = 0
# ------------------------------------------------------------------------
for n in range(num_episodes):
env.reset()
# Choose an action; Choose a bandit
action = actor(list(critic.model.values()))
if action in best_action:
num_best += 1
# Est. regret and save it
regret = estimate_regret(all_actions, action, critic)
# Pull a lever.
state, R_t, _, _ = env.step(action)
# Apply count bonus
if mode == "EB":
count_bonus = count(action)**(-0.5)
elif mode == "UCB":
count_bonus = ((2 * np.log(n + 1)) / count(action))**(0.5)
else:
raise ValueError("mode must be EB or UCB")
# Critic learns
payout = R_t + (beta * count_bonus)
critic = Q_update(action, payout, critic, lr_R)
# Log data
writer.add_scalar("state", int(state), n)
writer.add_scalar("action", action, n)
writer.add_scalar("regret", regret, n)
writer.add_scalar("bonus", count_bonus, n)
writer.add_scalar("score_R", R_t, n)
writer.add_scalar("value_R", critic(action), n)
total_R += R_t
total_regret += regret
writer.add_scalar("total_regret", total_regret, n)
writer.add_scalar("total_R", total_R, n)
writer.add_scalar("p_bests", num_best / (n + 1), n)
# --- Build the final result, and save or return it ---
writer.close()
result = dict(best=env.best,
beta=beta,
temp=temp,
mode=mode,
env_name=env_name,
num_episodes=num_episodes,
critic=critic.state_dict(),
count=count.state_dict(),
total_R=total_R,
total_regret=total_regret,
master_seed=master_seed)
if write_to_disk:
save_checkpoint(result,
filename=os.path.join(writer.log_dir, "result.pkl"))
return result
def run(env_name='InfoBlueYellow4b-v0',
num_episodes=1,
lr_E=1.0,
actor='DeterministicActor',
initial_count=1,
initial_bins=None,
initial_noise=0.0,
master_seed=None,
env_seed=None,
actor_seed=None,
critic_seed=None,
reward_mode=False,
log_dir=None,
write_to_disk=True,
**actor_kwargs):
"""Play some slots!"""
# --- Init ---
writer = SummaryWriter(log_dir=log_dir, write_to_disk=write_to_disk)
# -
if master_seed is not None:
env_seed = master_seed
critic_seed = master_seed
actor_seed = master_seed
# -
env = gym.make(env_name)
env.seed(env_seed)
num_actions = env.action_space.n
best_action = env.best
default_info_value = entropy(np.ones(num_actions) / num_actions)
E_t = default_info_value
# --- Agents and memories ---
# Critic
all_actions = list(range(num_actions))
critic_E = NoisyCritic(num_actions,
default_value=default_info_value,
default_noise_scale=initial_noise,
seed_value=critic_seed)
# Actor
if actor == "DeterministicActor":
actor_E = DeterministicActor(num_actions, **actor_kwargs)
elif actor == "SoftmaxActor":
actor_E = SoftmaxActor(num_actions,
**actor_kwargs,
seed_value=actor_seed)
elif actor == "RandomActor":
actor_E = RandomActor(num_actions,
**actor_kwargs,
seed_value=actor_seed)
elif actor == "ThresholdActor":
actor_E = ThresholdActor(num_actions,
**actor_kwargs,
seed_value=actor_seed)
else:
raise ValueError("actor was not a valid choice")
# Memory
memories = [
DiscreteDistribution(initial_bins=initial_bins,
initial_count=initial_count)
for _ in range(num_actions)
]
# --- Init log ---
num_best = 0
total_E = 0.0
total_regret = 0.0
# --- Main loop ---
for n in range(num_episodes):
# Each ep resets the env
env.reset()
# Choose a bandit arm
values = list(critic_E.model.values())
action = actor_E(values)
if action is None:
break
regret = estimate_regret(all_actions, action, critic_E)
# Pull a lever.
state, reward, _, _ = env.step(action)
# Estimate E, save regret
old = deepcopy(memories[action])
memories[action].update((int(state), int(reward)))
new = deepcopy(memories[action])
E_t = kl(new, old, critic_E.inital_values[action])
# --- Learn ---
critic_E = updateE(action, E_t, critic_E, lr=lr_E)
# --- Log data ---
num_stop = n
writer.add_scalar("state", int(state), n)
writer.add_scalar("regret", regret, n)
writer.add_scalar("score_E", E_t, n)
writer.add_scalar("value_E", critic_E(action), n)
total_E += E_t
total_regret += regret
writer.add_scalar("total_regret", total_regret, n)
writer.add_scalar("total_E", total_E, n)
if action in best_action:
num_best += 1
writer.add_scalar("p_bests", num_best / (n + 1), n)
writer.add_scalar("action", action, n)
tie = 0
if actor_E.tied:
tie = 1
writer.add_scalar("ties", tie, n)
# -- Build the final result, and save or return it ---
writer.close()
result = dict(best=best_action,
critic_E=critic_E.state_dict(),
intial_E=list(critic_E.inital_values.values()),
total_E=total_E,
total_regret=total_regret,
env_name=env_name,
num_episodes=num_episodes,
lr_E=lr_E,
master_seed=master_seed,
actor_seed=actor_seed,
critic_seed=critic_seed,
env_seed=env_seed,
initial_bins=initial_bins,
initial_count=initial_count,
actor=actor,
memories=[m.state_dict() for m in memories],
actor_kwargs=actor_kwargs,
num_stop=num_stop + 1)
if write_to_disk:
save_checkpoint(result,
filename=os.path.join(writer.log_dir, "result.pkl"))
return result
def run(env_name='BanditOneHigh2-v0',
num_episodes=1,
tie_break='next',
tie_threshold=0.0,
beta=1.0,
lr_R=.1,
master_seed=42,
log_dir=None,
write_to_disk=True):
"""Bandit agent - argmax(R + beta E)"""
# --- Init ---
writer = SummaryWriter(log_dir=log_dir, write_to_disk=write_to_disk)
# -
env = gym.make(env_name)
env.seed(master_seed)
num_actions = env.action_space.n
best_action = env.best
# -
default_reward_value = 0 # Null R
default_info_value = entropy(np.ones(num_actions) /
num_actions) # Uniform p(a)
E_t = default_info_value
R_t = default_reward_value
# Agents and memories
critic = Critic(num_actions,
default_value=default_reward_value +
(beta * default_info_value))
actor = Actor(num_actions,
tie_break=tie_break,
tie_threshold=tie_threshold)
memories = [DiscreteDistribution() for _ in range(num_actions)]
all_actions = list(range(num_actions))
# -
total_R = 0.0
total_E = 0.0
total_regret = 0.0
num_best = 0
# ------------------------------------------------------------------------
for n in range(num_episodes):
env.reset()
# Choose an action; Choose a bandit
action = actor(list(critic.model.values()))
if action in best_action:
num_best += 1
# Est. regret and save it
regret = estimate_regret(all_actions, action, critic)
# Pull a lever.
state, R_t, _, _ = env.step(action)
# Estimate E
old = deepcopy(memories[action])
memories[action].update((int(state), int(R_t)))
new = deepcopy(memories[action])
E_t = kl(new, old, default_info_value)
# Critic learns
critic = Q_update(action, R_t + (beta * E_t), critic, lr_R)
# Log data
writer.add_scalar("state", int(state), n)
writer.add_scalar("action", action, n)
writer.add_scalar("regret", regret, n)
writer.add_scalar("score_E", E_t, n)
writer.add_scalar("score_R", R_t, n)
writer.add_scalar("value_ER", critic(action), n)
total_E += E_t
total_R += R_t
total_regret += regret
writer.add_scalar("total_regret", total_regret, n)
writer.add_scalar("total_E", total_E, n)
writer.add_scalar("total_R", total_R, n)
writer.add_scalar("p_bests", num_best / (n + 1), n)
tie = 0
if actor.tied:
tie = 1
writer.add_scalar("ties", tie, n)
# -- Build the final result, and save or return it ---
writer.close()
result = dict(best=env.best,
beta=beta,
env_name=env_name,
num_episodes=num_episodes,
tie_break=tie_break,
tie_threshold=tie_threshold,
critic=critic.state_dict(),
memories=[m.state_dict() for m in memories],
total_E=total_E,
total_R=total_R,
total_regret=total_regret,
master_seed=master_seed)
if write_to_disk:
save_checkpoint(result,
filename=os.path.join(writer.log_dir, "result.pkl"))
return result
def run(env_name='BanditOneHot2-v0',
num_episodes=1,
lr_R=.1,
master_seed=42,
write_to_disk=True,
log_dir=None):
"""Bandit agent - random"""
# --- Init ---
writer = SummaryWriter(log_dir=log_dir, write_to_disk=write_to_disk)
# -
env = gym.make(env_name)
env.seed(master_seed)
num_actions = env.action_space.n
best_action = env.best
# -
default_reward_value = 0 # Null R
R_t = default_reward_value
critic = Critic(num_actions, default_value=default_reward_value)
actor = RandomActor(num_actions, seed_value=master_seed)
all_actions = list(range(num_actions))
# ------------------------------------------------------------------------
num_best = 0
total_R = 0.0
total_regret = 0.0
for n in range(num_episodes):
env.reset()
# Choose an action; Choose a bandit
action = actor(list(critic.model.values()))
if action in best_action:
num_best += 1
# Est. regret and save it
regret = estimate_regret(all_actions, action, critic)
# Pull a lever.
state, R_t, _, _ = env.step(action)
# Critic learns
critic = Q_update(action, R_t, critic, lr_R)
# Log data
writer.add_scalar("state", int(state), n)
writer.add_scalar("action", action, n)
writer.add_scalar("regret", regret, n)
writer.add_scalar("score_R", R_t, n)
writer.add_scalar("value_R", critic(action), n)
total_R += R_t
total_regret += regret
writer.add_scalar("total_regret", total_regret, n)
writer.add_scalar("total_R", total_R, n)
writer.add_scalar("p_bests", num_best / (n + 1), n)
# -- Build the final result, and save or return it ---
writer.close()
result = dict(best=env.best,
env_name=env_name,
num_episodes=num_episodes,
lr_R=lr_R,
critic=critic.state_dict(),
total_R=total_R,
total_regret=total_regret,
master_seed=master_seed)
if write_to_disk:
save_checkpoint(result,
filename=os.path.join(writer.log_dir, "result.pkl"))
return result
