--- a
+++ b/submission/baselines/ppo2/policies.py
@@ -0,0 +1,146 @@
+import numpy as np
+import tensorflow as tf
+from baselines.a2c.utils import conv, fc, conv_to_fc, batch_to_seq, seq_to_batch, lstm, lnlstm
+from submission.baselines.common.distributions import make_pdtype
+from baselines.common.input import observation_input
+
+def nature_cnn(unscaled_images, **conv_kwargs):
+    """
+    CNN from Nature paper.
+    """
+    scaled_images = tf.cast(unscaled_images, tf.float32) / 255.
+    activ = tf.nn.relu
+    h = activ(conv(scaled_images, 'c1', nf=32, rf=8, stride=4, init_scale=np.sqrt(2),
+                   **conv_kwargs))
+    h2 = activ(conv(h, 'c2', nf=64, rf=4, stride=2, init_scale=np.sqrt(2), **conv_kwargs))
+    h3 = activ(conv(h2, 'c3', nf=64, rf=3, stride=1, init_scale=np.sqrt(2), **conv_kwargs))
+    h3 = conv_to_fc(h3)
+    return activ(fc(h3, 'fc1', nh=512, init_scale=np.sqrt(2)))
+
+class LnLstmPolicy(object):
+    def __init__(self, sess, ob_space, ac_space, nbatch, nsteps, nlstm=256, reuse=False):
+        nenv = nbatch // nsteps
+        X, processed_x = observation_input(ob_space, nbatch)
+        M = tf.placeholder(tf.float32, [nbatch]) #mask (done t-1)
+        S = tf.placeholder(tf.float32, [nenv, nlstm*2]) #states
+        self.pdtype = make_pdtype(ac_space)
+        with tf.variable_scope("model", reuse=reuse):
+            h = nature_cnn(processed_x)
+            xs = batch_to_seq(h, nenv, nsteps)
+            ms = batch_to_seq(M, nenv, nsteps)
+            h5, snew = lnlstm(xs, ms, S, 'lstm1', nh=nlstm)
+            h5 = seq_to_batch(h5)
+            vf = fc(h5, 'v', 1)
+            self.pd, self.pi = self.pdtype.pdfromlatent(h5)
+
+        v0 = vf[:, 0]
+        a0 = self.pd.sample()
+        neglogp0 = self.pd.neglogp(a0)
+        self.initial_state = np.zeros((nenv, nlstm*2), dtype=np.float32)
+
+        def step(ob, state, mask):
+            return sess.run([a0, v0, snew, neglogp0], {X:ob, S:state, M:mask})
+
+        def value(ob, state, mask):
+            return sess.run(v0, {X:ob, S:state, M:mask})
+
+        self.X = X
+        self.M = M
+        self.S = S
+        self.vf = vf
+        self.step = step
+        self.value = value
+
+class LstmPolicy(object):
+
+    def __init__(self, sess, ob_space, ac_space, nbatch, nsteps, nlstm=256, reuse=False):
+        nenv = nbatch // nsteps
+        self.pdtype = make_pdtype(ac_space)
+        X, processed_x = observation_input(ob_space, nbatch)
+
+        M = tf.placeholder(tf.float32, [nbatch]) #mask (done t-1)
+        S = tf.placeholder(tf.float32, [nenv, nlstm*2]) #states
+        with tf.variable_scope("model", reuse=reuse):
+            h = nature_cnn(X)
+            xs = batch_to_seq(h, nenv, nsteps)
+            ms = batch_to_seq(M, nenv, nsteps)
+            h5, snew = lstm(xs, ms, S, 'lstm1', nh=nlstm)
+            h5 = seq_to_batch(h5)
+            vf = fc(h5, 'v', 1)
+            self.pd, self.pi = self.pdtype.pdfromlatent(h5)
+
+        v0 = vf[:, 0]
+        a0 = self.pd.sample()
+        neglogp0 = self.pd.neglogp(a0)
+        self.initial_state = np.zeros((nenv, nlstm*2), dtype=np.float32)
+
+        def step(ob, state, mask):
+            return sess.run([a0, v0, snew, neglogp0], {X:ob, S:state, M:mask})
+
+        def value(ob, state, mask):
+            return sess.run(v0, {X:ob, S:state, M:mask})
+
+        self.X = X
+        self.M = M
+        self.S = S
+        self.vf = vf
+        self.step = step
+        self.value = value
+
+class CnnPolicy(object):
+
+    def __init__(self, sess, ob_space, ac_space, nbatch, nsteps, reuse=False, **conv_kwargs): #pylint: disable=W0613
+        self.pdtype = make_pdtype(ac_space)
+        X, processed_x = observation_input(ob_space, nbatch)
+        with tf.variable_scope("model", reuse=reuse):
+            h = nature_cnn(processed_x, **conv_kwargs)
+            vf = fc(h, 'v', 1)[:,0]
+            self.pd, self.pi = self.pdtype.pdfromlatent(h, init_scale=0.01)
+
+        a0 = self.pd.sample()
+        neglogp0 = self.pd.neglogp(a0)
+        self.initial_state = None
+
+        def step(ob, *_args, **_kwargs):
+            a, v, neglogp = sess.run([a0, vf, neglogp0], {X:ob})
+            return a, v, self.initial_state, neglogp
+
+        def value(ob, *_args, **_kwargs):
+            return sess.run(vf, {X:ob})
+
+        self.X = X
+        self.vf = vf
+        self.step = step
+        self.value = value
+
+class MlpPolicy(object):
+    def __init__(self, sess, ob_space, ac_space, nbatch, nsteps, reuse=False): #pylint: disable=W0613
+        self.pdtype = make_pdtype(ac_space)
+        with tf.variable_scope("model", reuse=reuse):
+            X, processed_x = observation_input(ob_space, nbatch)
+            activ = tf.tanh
+            flatten = tf.layers.flatten
+            pi_h1 = activ(fc(flatten(X), 'pi_fc1', nh=64, init_scale=np.sqrt(2)))
+            pi_h2 = activ(fc(pi_h1, 'pi_fc2', nh=64, init_scale=np.sqrt(2)))
+            vf_h1 = activ(fc(flatten(X), 'vf_fc1', nh=64, init_scale=np.sqrt(2)))
+            vf_h2 = activ(fc(vf_h1, 'vf_fc2', nh=64, init_scale=np.sqrt(2)))
+            vf = fc(vf_h2, 'vf', 1)[:, 0]
+
+            self.pd, self.pi = self.pdtype.pdfromlatent(pi_h2, init_scale=0.01)
+
+
+        a0 = self.pd.sample()
+        neglogp0 = self.pd.neglogp(a0)
+        self.initial_state = None
+
+        def step(ob, *_args, **_kwargs):
+            a, v, neglogp = sess.run([a0, vf, neglogp0], {X:ob})
+            return a, v, self.initial_state, neglogp
+
+        def value(ob, *_args, **_kwargs):
+            return sess.run(vf, {X:ob})
+
+        self.X = X
+        self.vf = vf
+        self.step = step
+        self.value = value