diff --git a/baselines/ppo2/ppo2.py b/baselines/ppo2/ppo2.py
index d8ef42a5d5..9a1300385c 100644
--- a/baselines/ppo2/ppo2.py
+++ b/baselines/ppo2/ppo2.py
@@ -218,7 +218,7 @@ def f(_):
         return val
     return f
 
-def learn(*, network, env, total_timesteps, seed=None, nsteps=2048, ent_coef=0.0, lr=3e-4,
+def learn(*, network, env, total_timesteps, eval_env = None, seed=None, nsteps=2048, ent_coef=0.0, lr=3e-4,
             vf_coef=0.5,  max_grad_norm=0.5, gamma=0.99, lam=0.95,
             log_interval=10, nminibatches=4, noptepochs=4, cliprange=0.2,
             save_interval=0, load_path=None, **network_kwargs):
@@ -307,8 +307,14 @@ def learn(*, network, env, total_timesteps, seed=None, nsteps=2048, ent_coef=0.0
         model.load(load_path)
     # Instantiate the runner object
     runner = Runner(env=env, model=model, nsteps=nsteps, gamma=gamma, lam=lam)
+    if eval_env is not None:
+        eval_runner = Runner(env = eval_env, model = model, nsteps = nsteps, gamma = gamma, lam= lam)
+
+
 
     epinfobuf = deque(maxlen=100)
+    if eval_env is not None:
+        eval_epinfobuf = deque(maxlen=100)
 
     # Start total timer
     tfirststart = time.time()
@@ -325,7 +331,12 @@ def learn(*, network, env, total_timesteps, seed=None, nsteps=2048, ent_coef=0.0
         cliprangenow = cliprange(frac)
         # Get minibatch
         obs, returns, masks, actions, values, neglogpacs, states, epinfos = runner.run() #pylint: disable=E0632
+        if eval_env is not None:
+            eval_obs, eval_returns, eval_masks, eval_actions, eval_values, eval_neglogpacs, eval_states, eval_epinfos = eval_runner.run() #pylint: disable=E0632
+
         epinfobuf.extend(epinfos)
+        if eval_env is not None:
+            eval_epinfobuf.extend(eval_epinfos)
 
         # Here what we're going to do is for each minibatch calculate the loss and append it.
         mblossvals = []
@@ -375,6 +386,9 @@ def learn(*, network, env, total_timesteps, seed=None, nsteps=2048, ent_coef=0.0
             logger.logkv("explained_variance", float(ev))
             logger.logkv('eprewmean', safemean([epinfo['r'] for epinfo in epinfobuf]))
             logger.logkv('eplenmean', safemean([epinfo['l'] for epinfo in epinfobuf]))
+            if eval_env is not None:
+                logger.logkv('eval_eprewmean', safemean([epinfo['r'] for epinfo in eval_epinfobuf]) )
+                logger.logkv('eval_eplenmean', safemean([epinfo['l'] for epinfo in eval_epinfobuf]) )
             logger.logkv('time_elapsed', tnow - tfirststart)
             for (lossval, lossname) in zip(lossvals, model.loss_names):
                 logger.logkv(lossname, lossval)