Fixes.

Denys88 · Sep 25, 2023 · 872e767 · 872e767
1 parent 06756a0
commit 872e767
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Showing 3 changed files with 5 additions and 4 deletions.
diff --git a/README.md b/README.md
@@ -305,6 +305,7 @@ Additional environment supported properties and functions
 * Added evaluation feature for inferencing during training. Checkpoints from training process can be automatically picked up and updated in the inferencing process when enabled.
 * Added get/set API for runtime update of rl training parameters. Thanks to @ArthurAllshire for the initial version of fast PBT code.
 * Fixed SAC not loading weights properly.
+* Removed Ray dependency for use cases it's not required.
 
 1.6.0
 

diff --git a/rl_games/algos_torch/network_builder.py b/rl_games/algos_torch/network_builder.py
@@ -323,7 +323,7 @@ def forward(self, obs_dict):
                 c_out = c_out.contiguous().view(c_out.size(0), -1)                    
 
                 if self.has_rnn:
-                    seq_length = obs_dict.get['seq_length']
+                    seq_length = obs_dict['seq_length']
 
                     if not self.is_rnn_before_mlp:
                         a_out_in = a_out
@@ -398,7 +398,7 @@ def forward(self, obs_dict):
                 out = out.flatten(1)                
 
                 if self.has_rnn:
-                    seq_length = obs_dict.get['seq_length']
+                    seq_length = obs_dict['seq_length']
 
                     out_in = out
                     if not self.is_rnn_before_mlp:
@@ -712,7 +712,7 @@ def forward(self, obs_dict):
             out = self.flatten_act(out)
 
             if self.has_rnn:
-                seq_length = obs_dict.get['seq_length']
+                seq_length = obs_dict['seq_length']
 
                 out_in = out
                 if not self.is_rnn_before_mlp:

diff --git a/rl_games/common/a2c_common.py b/rl_games/common/a2c_common.py
@@ -466,7 +466,7 @@ def init_tensors(self):
             self.rnn_states = [s.to(self.ppo_device) for s in self.rnn_states]
 
             total_agents = self.num_agents * self.num_actors
-            num_seqs = self.horizon_length // self.seq_len
+            num_seqs = self.horizon_length // self.seq_length
             assert((self.horizon_length * total_agents // self.num_minibatches) % self.seq_length == 0)
             self.mb_rnn_states = [torch.zeros((num_seqs, s.size()[0], total_agents, s.size()[2]), dtype = torch.float32, device=self.ppo_device) for s in self.rnn_states]