def train_double_dqn(env, num_actions):
results_dir = './results/double_dqn/wind_tunnel'
training_epsilon = 0.01
test_epsilon = 0.001
frame_history = 1
dqn = atari_dqn.AtariDQN(frame_history, num_actions)
agent = dq_learner.DQLearner(dqn,
num_actions,
frame_history=frame_history,
epsilon_end=training_epsilon)
train(agent, env, test_epsilon, results_dir)
def train_dqn(env, num_actions):
results_dir = './results/dqn/coin_game'
training_epsilon = 0.1
test_epsilon = 0.05
frame_history = 1
dqn = atari_dqn.AtariDQN(frame_history, num_actions, shared_bias=False)
agent = dq_learner.DQLearner(dqn,
num_actions,
target_copy_freq=10000,
epsilon_end=training_epsilon,
double=False,
frame_history=frame_history)
train(agent, env, test_epsilon, results_dir)
def train_tabular_dqn(env, num_actions):
results_dir = './results/dqn/tab_coin_game_lr0.0025_rp10000'
training_epsilon = 0.1
test_epsilon = 0.05
n = 3
frame_history = 1
dqn = tabular_dqn.TabularDQN(n,
frame_history,
num_actions,
shared_bias=False)
agent = dq_learner.DQLearner(dqn,
num_actions,
target_copy_freq=3000,
epsilon_end=training_epsilon,
double=False,
frame_history=frame_history,
learning_rate=0.0025,
replay_start_size=10000,
epsilon_steps=100000.,
replay_memory_size=10001)
train(agent, env, test_epsilon, results_dir)
