Add a RL actor-critic like algorithm for benchmarking

This algorithm uses a variant of experience replay,
and one policy per option to predict.

Author: Jules Pondard

python/experimental/options_search/rl_memory_replay.py

@@ -0,0 +1,171 @@
+import numpy as np
+import torch
+import torch.optim as optim
+import torch.nn as nn
+import torch.utils.data
+import torch.nn.functional as F
+#import ipdb
+from itertools import count
+from collections import namedtuple
+from torch.distributions import Categorical
+import tensor_comprehensions as tc
+from visdom import Visdom
+from collections import deque
+from heapq import heappush, heappop
+
+import utils
+
+NB_EPOCHS = 1000
+BATCH_SZ = 16
+buff = deque()
+MAXI_BUFF_SZ = 50
+
+(tc_code, tc_name, inp, init_input_sz) = utils.get_convolution_example(size_type="input", inp_sz_list=[8,2,28,28,8,1,1])
+
+NB_HYPERPARAMS, INIT_INPUT_SZ = utils.NB_HYPERPARAMS, utils.INIT_INPUT_SZ
+
+viz = Visdom()
+win0 = viz.line(X=np.arange(NB_EPOCHS), Y=np.random.rand(NB_EPOCHS))
+win1 = viz.line(X=np.arange(NB_EPOCHS), Y=np.random.rand(NB_EPOCHS))
+
+SavedAction = namedtuple('SavedAction', ['log_prob', 'value'])
+
+layer_sz = 32
+
+class Predictor(nn.Module):
+    def __init__(self, nb_inputs, nb_actions):
+        super(Predictor, self).__init__()
+        self.affine1 = nn.Linear(nb_inputs, layer_sz)
+        self.affine15 = nn.Linear(layer_sz, layer_sz)
+        self.affine2 = nn.Linear(layer_sz, nb_actions)
+        self.affine3 = nn.Linear(layer_sz, 1)
+
+        self.W = nn.Linear(nb_inputs, nb_inputs)
+
+    def forward(self, x):
+        #ipdb.set_trace()
+        #x = F.softmax(self.W(x), dim=-1) * x #attention mecanism
+        tmp1 = F.relu(self.affine1(x))
+        #tmp1 = F.relu(self.affine15(tmp1))
+        out_action = F.softmax(self.affine2(tmp1), dim=-1)
+        out_value = self.affine3(tmp1)
+        return out_action, out_value
+
+class FullNetwork(nn.Module):
+    def __init__(self, nb_hyperparams, init_input_sz):
+        super(FullNetwork, self).__init__()
+        self.nb_hyperparams = nb_hyperparams
+        self.init_input_sz = init_input_sz
+        self.nets = [Predictor(init_input_sz + i, int(utils.cat_sz[i])) for i in range(nb_hyperparams)]
+        self.nets = nn.ModuleList(self.nets)
+
+    def select_action(self, x, i, out_sz):
+        geps = 0.1
+        proba = np.random.rand()
+        probs, state_value = self.nets[i](x)
+        if(proba <= geps):
+            probs = torch.FloatTensor([1./out_sz]*out_sz)
+        m = Categorical(probs)
+        action = m.sample()
+        return action.item(), m.log_prob(action), state_value
+
+    def forward(self, x):
+        actions_prob = []
+        values = []
+        for i in range(self.nb_hyperparams):
+            sym, action_prob, value = self.select_action(x, i, int(utils.cat_sz[i]))
+            actions_prob.append(action_prob)
+            values.append(value)
+            x = torch.cat([x, torch.FloatTensor([sym])])
+        return x[INIT_INPUT_SZ:], actions_prob, values
+
+net = FullNetwork(NB_HYPERPARAMS, INIT_INPUT_SZ)
+optimizer = optim.Adam(net.parameters(), lr=0.0001)
+eps = np.finfo(np.float32).eps.item()
+
+#print(utils.getAllDivs(inp))
+
+def finish_episode(actions_probs, values, final_rewards):
+    policy_losses = [[] for i in range(BATCH_SZ)]
+    value_losses = [[] for i in range(BATCH_SZ)]
+    final_rewards = torch.tensor(list(final_rewards))
+    #final_rewards = (final_rewards - final_rewards.mean()) / (final_rewards.std() + eps)
+    for batch_id in range(BATCH_SZ):
+        for (log_prob, value) in zip(actions_probs[batch_id], values[batch_id]):
+            reward = final_rewards[batch_id] - value.item()
+            policy_losses[batch_id].append(-log_prob * reward)
+            value_losses[batch_id].append(F.smooth_l1_loss(value, torch.tensor([final_rewards[batch_id]])))
+    optimizer.zero_grad()
+    vloss = torch.stack([torch.stack(value_losses[i]).sum() for i in range(BATCH_SZ)]).mean()
+    ploss = torch.stack([torch.stack(policy_losses[i]).sum() for i in range(BATCH_SZ)]).mean()
+    loss = ploss + vloss
+    loss.backward(retain_graph=True)
+    optimizer.step()
+    return vloss.item(), ploss.item()
+
+def add_to_buffer(actions_probs, values, reward):
+    global buff
+    #if(len(buff) > 0):
+    #    min_reward = np.min(np.array(buff)[:,2])
+    #    if(reward < 10*min_reward):
+    #        return
+    if len(buff) == MAXI_BUFF_SZ:
+        #heappop(buff)
+        buff.popleft()
+    #heappush(buff, (reward, actions_probs, values))
+    buff.append((reward, actions_probs, values))
+
+def select_batch():
+    #random.sample()
+    batch = [buff[np.random.randint(len(buff))] for i in range(BATCH_SZ)]
+    #batch.append(buff[-1])
+    batch=np.array(batch)
+    return batch[:,1], batch[:,2], batch[:,0]
+
+def get_best_buff():
+    return np.max(np.array(buff)[:,0])
+
+INTER_DISP = 20
+
+running_reward = -0.5
+tab_rewards=[]
+tab_best=[]
+best=-12
+v_losses=[]
+p_losses=[]
+best_options = np.zeros(NB_HYPERPARAMS).astype(int)
+for i in range(NB_EPOCHS):
+    rewards = []
+    out_actions, out_probs, out_values = net(init_input_sz)
+    #utils.print_opt(out_actions.numpy().astype(int))
+    reward = utils.evalTime(out_actions.numpy().astype(int), prune=-1, curr_best=np.exp(-best))
+    #reward=100*reward
+    #reward = -((reward)/1000)
+    reward = -np.log(reward)
+    add_to_buffer(out_probs, out_values, reward)
+    best_in_buffer = get_best_buff()
+    if(i >= 20):
+        actions_probs, values, rewards = select_batch()
+        for j in range(1):
+            vloss, ploss = finish_episode(actions_probs, values, rewards)
+        v_losses.append(vloss)
+        p_losses.append(ploss)
+    if(best < reward or i==0):
+        best=reward
+        best_options = out_actions.numpy().astype(int)
+        utils.print_opt(best_options)
+    if(i==0):
+        running_reward = reward
+    running_reward = running_reward * 0.99 + reward * 0.01
+    tab_rewards.append(-(running_reward))
+    tab_best.append(-best)
+    if i % INTER_DISP == 0:
+        viz.line(X=np.column_stack((np.arange(i+1), np.arange(i+1))), Y=np.column_stack((np.array(tab_rewards), np.array(tab_best))), win=win0, opts=dict(legend=["Geometric run", "Best time"]))
+        if(len(v_losses) > 0):
+            viz.line(X=np.column_stack((np.arange(len(v_losses)), np.arange(len(v_losses)))), Y=np.column_stack((np.array(v_losses), np.array(p_losses))), win=win1, opts=dict(legend=["Value loss", "Policy loss"]))
+    print(-running_reward)
+    print(-best)
+    print("Best in buffer: " + str(-best_in_buffer))
+
+print("Finally, best options are:")
+utils.print_opt(best_options)