add flattening logic so ddpg can handle image observations #23
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| @@ -14,7 +14,7 @@ def rollout(env, agent, max_path_length=np.inf, animated=False, speedup=1): | |||
| if animated: | |||
| env.render() | |||
| while path_length < max_path_length: | |||
| a, agent_info = agent.get_action(o) | |||
| a, agent_info = agent.get_action(env.observation_space.flatten(o)) | |||
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This would break quite a few things since the get_action method is supposed to receive just the raw actions. Why is this change needed?
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without this change I get the following error:
Traceback (most recent call last):
File "/home/tom/rllab/scripts/run_experiment_lite.py", line 103, in
run_experiment(sys.argv)
File "/home/tom/rllab/scripts/run_experiment_lite.py", line 90, in run_experiment
maybe_iter = concretize(data)
File "/home/tom/rllab/rllab/misc/instrument.py", line 898, in concretize
return method(_args, *_kwargs)
File "/home/tom/rllab/rllab/algos/ddpg.py", line 263, in train
self.evaluate(epoch, pool)
File "/home/tom/rllab/rllab/algos/ddpg.py", line 381, in evaluate
max_path_length=self.max_path_length,
File "/home/tom/rllab/rllab/sampler/parallel_sampler.py", line 114, in sample_paths
show_prog_bar=True
File "/home/tom/rllab/rllab/sampler/stateful_pool.py", line 142, in run_collect
result, inc = collect_once(self.G, *args)
File "/home/tom/rllab/rllab/sampler/parallel_sampler.py", line 89, in _worker_collect_one_path
path = rollout(G.env, G.policy, max_path_length)
File "/home/tom/rllab/rllab/sampler/utils.py", line 17, in rollout
a, agent_info = agent.get_action(o)
File "/home/tom/rllab/rllab/policies/deterministic_mlp_policy.py", line 66, in get_action
action = self._f_actions([observation])[0]
File "/home/tom/anaconda2/envs/rllab/lib/python2.7/site-packages/theano/compile/function_module.py", line 784, in call
allow_downcast=s.allow_downcast)
File "/home/tom/anaconda2/envs/rllab/lib/python2.7/site-packages/theano/tensor/type.py", line 178, in filter
data.shape))
TypeError: ('Bad input argument to theano function with name "/home/tom/rllab/rllab/misc/ext.py:135" at index 0 (0-based)', 'Wrong number of dimensions: expected 2, got 4 with shape (1, 96, 96, 3).')
Or in short, the policy network is expecting a flat input but getting a non-flat input. An alternative would be to handle flattening inside the get_action function of DeterministicMLPPolicy.
| @@ -224,7 +224,7 @@ def train(self): | |||
| self.es_path_returns.append(path_return) | |||
| path_length = 0 | |||
| path_return = 0 | |||
| action = self.es.get_action(itr, observation, policy=sample_policy) # qf=qf) | |||
| action = self.es.get_action(itr, self.env.observation_space.flatten(observation), policy=sample_policy) # qf=qf) | |||
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Just to be a little consistent, could this be changed to passing the raw observations to exploration strategy? (shouldn't require any other changes, as I believe that none of the exploration strategies implemented make use of the observations...)
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Thanks, merged! |
Logic to flatten observations that are not a flat vector, allowing the ddpg algorithm to work with environments that return image data in their observations. cf. #22.