num_frames = 1000000
batch_size = 32
gamma = 0.99
record_idx = 10000
replay_initial = 10000
replay_buffer = ReplayBuffer(100000)
model = QLearner(env, num_frames, batch_size, gamma, replay_buffer)
model.load_state_dict(torch.load("model_pretrained.pth", map_location='cpu'))
target_model = QLearner(env, num_frames, batch_size, gamma, replay_buffer)
target_model.copy_from(model)
optimizer = optim.Adam(model.parameters(), lr=0.00001)
if USE_CUDA:
model = model.cuda()
target_model = target_model.cuda()
print("Using cuda")
epsilon_start = 1.0
epsilon_final = 0.01
epsilon_decay = 30000
epsilon_by_frame = lambda frame_idx: epsilon_final + (
epsilon_start - epsilon_final) * math.exp(-1. * frame_idx / epsilon_decay)
# .01 + 0.99* 1/ e^(frame index / 30000)
# So epsilon starts at pretty much 1
# as frame index increases, exp will get larger so 1/exp will decrease, so the 0.99 term will decrease, leaving us with just the final
# half of num_frames will give us a value very close to final value. So it decays quickly.
losses = []
all_rewards = []
replay_initial = 10000 # number frames that are held
replay_buffer = ReplayBuffer(100000)
model = QLearner(env, num_frames, batch_size, gamma, replay_buffer)
model.load_state_dict(
torch.load("model_pretrained.pth",
map_location='cpu')) #loading in the pretrained model
target_model = QLearner(env, num_frames, batch_size, gamma,
replay_buffer) #load in model
target_model.copy_from(model)
optimizer = optim.Adam(model.parameters(),
lr=0.0001) #learning rate set and optimizing the model
if USE_CUDA:
model = model.cuda() # sends model to gpu
target_model = target_model.cuda()
print("Using cuda")
epsilon_start = 1.0
epsilon_final = 0.01
epsilon_decay = 30000 #used in ?
epsilon_by_frame = lambda frame_idx: epsilon_final + (
epsilon_start - epsilon_final) * math.exp(-1. * frame_idx / epsilon_decay)
losses = []
all_rewards = []
episode_reward = 0
state = env.reset() # initial state
env = make_atari(env_id)
env = wrap_deepmind(env)
env = wrap_pytorch(env)
num_frames = 1000000
batch_size = 32
gamma = 0.99
replay_initial = 10000
replay_buffer = ReplayBuffer(100000)
model = QLearner(env, num_frames, batch_size, gamma, replay_buffer)
model.load_state_dict(torch.load('trained_model.pth'))
model.eval()
if USE_CUDA:
model = model.cuda()
epsilon_start = 1.0
epsilon_final = 0.01
epsilon_decay = 30000
losses = []
all_rewards = []
episode_reward = 0
loss_list = []
reward_list = []
state = env.reset()
frame_list = random.sample(range(2000, num_frames), 8000)
