removed tensorflow dependency

Author: ryan-lowe

README.md

@@ -10,7 +10,7 @@ Used in the paper [Multi-Agent Actor-Critic for Mixed Cooperative-Competitive En
 - To interactively view moving to landmark scenario (see others in ./scenarios/):
 `bin/interactive.py --scenario simple.py`
 
-- Known dependencies: OpenAI gym, numpy, tensorflow
+- Known dependencies: OpenAI gym, numpy
 
 - To use the environments, look at the code for importing them in `make_env.py`.
 

multiagent/environment.py

@@ -2,7 +2,6 @@
 from gym import spaces
 from gym.envs.registration import EnvSpec
 import numpy as np
-import tensorflow as tf
 
 # environment for all agents in the multiagent world
 # currently code assumes that no agents will be created/destroyed at runtime!
@@ -281,15 +280,6 @@ def _make_receptor_locations(self, agent):
  dx.append(np.array([x,y]))
  return dx
 
- # construct receptive field activations for 'other' relative to root
- def _get_receptor_activations(self, entity_root, state_root, other_x_pos):
- activations = []
- for dx in self.receptor_locations:
- dx_pos = state_root.x_pos + dx - other_x_pos
- activations.append(tf.reduce_sum(tf.square(dx_pos), 1, keep_dims=True))
- activations = tf.concat(1, activations)
- #return tf.nn.softmax(-1e+1 * activations)
- return tf.exp(-activations / 1.0**2)
 
 # vectorized wrapper for a batch of multi-agent environments
 # assumes all environments have the same observation and action space