diff --git a/q_transformer/mocks.py b/q_transformer/mocks.py
index 6652582..fd849de 100644
--- a/q_transformer/mocks.py
+++ b/q_transformer/mocks.py
@@ -7,10 +7,12 @@ class MockReplayDataset(Dataset):
     def __init__(
         self,
         length = 10000,
+        num_actions = 1,
         num_action_bins = 256,
         video_shape = (6, 224, 224)
     ):
         self.length = length
+        self.num_actions = num_actions
         self.num_action_bins = num_action_bins
         self.video_shape = video_shape
 
@@ -21,7 +23,12 @@ def __getitem__(self, _):
 
         instruction = "please clean the kitchen"
         state = torch.randn(3, *self.video_shape)
-        action = torch.tensor(randrange(self.num_action_bins + 1))
+
+        if self.num_actions == 1:
+            action = torch.tensor(randrange(self.num_action_bins + 1))
+        else:
+            action = torch.randint(0, self.num_action_bins + 1, (self.num_actions,))
+
         next_state = torch.randn(3, *self.video_shape)
         reward = torch.tensor(randrange(2))
         done = torch.tensor(randrange(2), dtype = torch.bool)
@@ -33,12 +40,14 @@ def __init__(
         self,
         length = 10000,
         num_steps = 2,
+        num_actions = 1,
         num_action_bins = 256,
         video_shape = (6, 224, 224)
     ):
         self.num_steps = num_steps
         self.time_shape = (num_steps,)
         self.length = length
+        self.num_actions = num_actions
         self.num_action_bins = num_action_bins
         self.video_shape = video_shape
 
@@ -47,9 +56,11 @@ def __len__(self):
 
     def __getitem__(self, _):
 
+        action_dims = (self.num_actions,) if self.num_actions > 1 else tuple()
+
         instruction = "please clean the kitchen"
         state = torch.randn(*self.time_shape, 3, *self.video_shape)
-        action = torch.randint(0, self.num_action_bins + 1, self.time_shape)
+        action = torch.randint(0, self.num_action_bins + 1, (*action_dims, *self.time_shape))
         next_state = torch.randn(3, *self.video_shape)
         reward = torch.randint(0, 2, self.time_shape)
         done = torch.zeros(self.time_shape, dtype = torch.bool)
diff --git a/q_transformer/q_transformer.py b/q_transformer/q_transformer.py
index 24892e2..b33f557 100644
--- a/q_transformer/q_transformer.py
+++ b/q_transformer/q_transformer.py
@@ -252,7 +252,7 @@ def q_learn(
         reward:         TensorType['b', float],
         done:           TensorType['b', bool],
         *,
-        monte_carlo_return = None
+        monte_carlo_return = -1e4
 
     ) -> Tuple[Tensor, QIntermediates]:
         # 'next' stands for the very next time step (whether state, q, actions etc)
@@ -270,7 +270,7 @@ def q_learn(
         # the max Q value is taken as the optimal action is implicitly the one with the highest Q score
 
         q_next = self.ema_model(next_states, instructions).amax(dim = -1)
-        q_next = q_next.clamp(min = default(monte_carlo_return, -1e4))
+        q_next = q_next.clamp(min = monte_carlo_return)
 
         # Bellman's equation. most important line of code, hopefully done correctly
 
@@ -294,7 +294,7 @@ def n_step_q_learn(
         rewards:        TensorType['b', 't', float],
         dones:          TensorType['b', 't', bool],
         *,
-        monte_carlo_return = None
+        monte_carlo_return = -1e4
 
     ) -> Tuple[Tensor, QIntermediates]:
         """
@@ -338,7 +338,7 @@ def n_step_q_learn(
         q_pred = unpack_one(q_pred, time_ps, '*')
 
         q_next = self.ema_model(next_states, instructions).amax(dim = -1)
-        q_next = q_next.clamp(min = default(monte_carlo_return, -1e4))
+        q_next = q_next.clamp(min = monte_carlo_return)
 
         # prepare rewards and discount factors across timesteps
 
@@ -369,7 +369,7 @@ def autoregressive_q_learn(
         rewards:        TensorType['b', 't', float],
         dones:          TensorType['b', 't', bool],
         *,
-        monte_carlo_return = None
+        monte_carlo_return = -1e4
 
     ) -> Tuple[Tensor, QIntermediates]:
         """
@@ -386,6 +386,18 @@ def autoregressive_q_learn(
         q - q values
         """
 
+        num_timesteps, device = states.shape[1], states.device
+
+        # fold time steps into batch
+
+        states, time_ps = pack_one(states, '* c f h w')
+
+        # repeat instructions per timestep
+
+        repeated_instructions = repeat_tuple_el(instructions, num_timesteps)
+
+        γ = self.discount_factor_gamma
+
         raise NotImplementedError
 
     def learn(