[rllib] Fix LSTM regression on truncated sequences and add regression test

python/ray/rllib/env/atari_wrappers.py

@@ -7,6 +7,10 @@
 
 
 def is_atari(env):
+    if (hasattr(env.observation_space, "shape")
+            and env.observation_space.shape is not None
+            and len(env.observation_space.shape) <= 2):
+        return False
     return hasattr(env, "unwrapped") and hasattr(env.unwrapped, "ale")
 
 

python/ray/rllib/examples/cartpole_lstm.py

@@ -0,0 +1,179 @@
+"""Partially observed variant of the CartPole gym environment.
+
+https://github.com/openai/gym/blob/master/gym/envs/classic_control/cartpole.py
+
+We delete the velocity component of the state, so that it can only be solved
+by a LSTM policy."""
+
+import argparse
+import math
+import gym
+from gym import spaces
+from gym.utils import seeding
+import numpy as np
+
+parser = argparse.ArgumentParser()
+parser.add_argument("--stop", type=int, default=200)
+
+
+class CartPoleStatelessEnv(gym.Env):
+    metadata = {
+        'render.modes': ['human', 'rgb_array'],
+        'video.frames_per_second': 60
+    }
+
+    def __init__(self):
+        self.gravity = 9.8
+        self.masscart = 1.0
+        self.masspole = 0.1
+        self.total_mass = (self.masspole + self.masscart)
+        self.length = 0.5  # actually half the pole's length
+        self.polemass_length = (self.masspole * self.length)
+        self.force_mag = 10.0
+        self.tau = 0.02  # seconds between state updates
+
+        # Angle at which to fail the episode
+        self.theta_threshold_radians = 12 * 2 * math.pi / 360
+        self.x_threshold = 2.4
+
+        high = np.array([
+            self.x_threshold * 2,
+            self.theta_threshold_radians * 2,
+        ])
+
+        self.action_space = spaces.Discrete(2)
+        self.observation_space = spaces.Box(-high, high)
+
+        self.seed()
+        self.viewer = None
+        self.state = None
+
+        self.steps_beyond_done = None
+
+    def seed(self, seed=None):
+        self.np_random, seed = seeding.np_random(seed)
+        return [seed]
+
+    def step(self, action):
+        assert self.action_space.contains(
+            action), "%r (%s) invalid" % (action, type(action))
+        state = self.state
+        x, x_dot, theta, theta_dot = state
+        force = self.force_mag if action == 1 else -self.force_mag
+        costheta = math.cos(theta)
+        sintheta = math.sin(theta)
+        temp = (force + self.polemass_length * theta_dot * theta_dot * sintheta
+                ) / self.total_mass
+        thetaacc = (self.gravity * sintheta - costheta * temp) / (
+            self.length *
+            (4.0 / 3.0 - self.masspole * costheta * costheta / self.total_mass)
+        )
+        xacc = (temp -
+                self.polemass_length * thetaacc * costheta / self.total_mass)
+        x = x + self.tau * x_dot
+        x_dot = x_dot + self.tau * xacc
+        theta = theta + self.tau * theta_dot
+        theta_dot = theta_dot + self.tau * thetaacc
+        self.state = (x, x_dot, theta, theta_dot)
+        done = (x < -self.x_threshold or x > self.x_threshold
+                or theta < -self.theta_threshold_radians
+                or theta > self.theta_threshold_radians)
+        done = bool(done)
+
+        if not done:
+            reward = 1.0
+        elif self.steps_beyond_done is None:
+            # Pole just fell!
+            self.steps_beyond_done = 0
+            reward = 1.0
+        else:
+            self.steps_beyond_done += 1
+            reward = 0.0
+
+        rv = np.r_[self.state[0], self.state[2]]
+        return rv, reward, done, {}
+
+    def reset(self):
+        self.state = self.np_random.uniform(low=-0.05, high=0.05, size=(4, ))
+        self.steps_beyond_done = None
+
+        rv = np.r_[self.state[0], self.state[2]]
+        return rv
+
+    def render(self, mode='human'):
+        screen_width = 600
+        screen_height = 400
+
+        world_width = self.x_threshold * 2
+        scale = screen_width / world_width
+        carty = 100  # TOP OF CART
+        polewidth = 10.0
+        polelen = scale * 1.0
+        cartwidth = 50.0
+        cartheight = 30.0
+
+        if self.viewer is None:
+            from gym.envs.classic_control import rendering
+            self.viewer = rendering.Viewer(screen_width, screen_height)
+            l, r, t, b = (-cartwidth / 2, cartwidth / 2, cartheight / 2,
+                          -cartheight / 2)
+            axleoffset = cartheight / 4.0
+            cart = rendering.FilledPolygon([(l, b), (l, t), (r, t), (r, b)])
+            self.carttrans = rendering.Transform()
+            cart.add_attr(self.carttrans)
+            self.viewer.add_geom(cart)
+            l, r, t, b = (-polewidth / 2, polewidth / 2,
+                          polelen - polewidth / 2, -polewidth / 2)
+            pole = rendering.FilledPolygon([(l, b), (l, t), (r, t), (r, b)])
+            pole.set_color(.8, .6, .4)
+            self.poletrans = rendering.Transform(translation=(0, axleoffset))
+            pole.add_attr(self.poletrans)
+            pole.add_attr(self.carttrans)
+            self.viewer.add_geom(pole)
+            self.axle = rendering.make_circle(polewidth / 2)
+            self.axle.add_attr(self.poletrans)
+            self.axle.add_attr(self.carttrans)
+            self.axle.set_color(.5, .5, .8)
+            self.viewer.add_geom(self.axle)
+            self.track = rendering.Line((0, carty), (screen_width, carty))
+            self.track.set_color(0, 0, 0)
+            self.viewer.add_geom(self.track)
+
+        if self.state is None:
+            return None
+
+        x = self.state
+        cartx = x[0] * scale + screen_width / 2.0  # MIDDLE OF CART
+        self.carttrans.set_translation(cartx, carty)
+        self.poletrans.set_rotation(-x[2])
+
+        return self.viewer.render(return_rgb_array=mode == 'rgb_array')
+
+    def close(self):
+        if self.viewer:
+            self.viewer.close()
+
+
+if __name__ == "__main__":
+    import ray
+    from ray import tune
+
+    args = parser.parse_args()
+
+    tune.register_env("cartpole_stateless", lambda _: CartPoleStatelessEnv())
+
+    ray.init()
+    tune.run_experiments({
+        "test": {
+            "env": "cartpole_stateless",
+            "run": "PG",
+            "stop": {
+                "episode_reward_mean": args.stop
+            },
+            "config": {
+                "model": {
+                    "use_lstm": True,
+                },
+            },
+        }
+    })

python/ray/rllib/models/lstm.py

@@ -156,9 +156,11 @@ def _build_layers(self, inputs, num_outputs, options):
             self.state_in = [c_in, h_in]
 
         # Setup LSTM outputs
+        state_in = rnn.LSTMStateTuple(c_in, h_in)
         lstm_out, lstm_state = tf.nn.dynamic_rnn(
             lstm,
             last_layer,
+            initial_state=state_in,
             sequence_length=self.seq_lens,
             time_major=False,
             dtype=tf.float32)

python/ray/tune/visual_utils.py

@@ -36,7 +36,7 @@ def _parse_results(res_path):
             for line in f:
                 pass
         res_dict = _flatten_dict(json.loads(line.strip()))
-    except Exception as e:
+    except Exception:
         logger.exception("Importing %s failed...Perhaps empty?" % res_path)
     return res_dict
 
@@ -45,7 +45,7 @@ def _parse_configs(cfg_path):
     try:
         with open(cfg_path) as f:
             cfg_dict = _flatten_dict(json.load(f))
-    except Exception as e:
+    except Exception:
         logger.exception("Config parsing failed.")
     return cfg_dict
 

test/jenkins_tests/run_multi_node_tests.sh

@@ -18,6 +18,13 @@ docker run  -e "RAY_USE_XRAY=1" --rm --shm-size=10G --memory=10G $DOCKER_SHA \
     --stop '{"training_iteration": 2}' \
     --config '{"num_workers": 2}'
 
+docker run  -e "RAY_USE_XRAY=1" --rm --shm-size=10G --memory=10G $DOCKER_SHA \
+    python /ray/python/ray/rllib/train.py \
+    --env Pong-ram-v4 \
+    --run A3C \
+    --stop '{"training_iteration": 2}' \
+    --config '{"num_workers": 2}'
+
 docker run  -e "RAY_USE_XRAY=1" --rm --shm-size=10G --memory=10G $DOCKER_SHA \
     python /ray/python/ray/rllib/train.py \
     --env PongDeterministic-v0 \
@@ -293,6 +300,9 @@ docker run  -e "RAY_USE_XRAY=1" --rm --shm-size=10G --memory=10G $DOCKER_SHA \
 docker run  -e "RAY_USE_XRAY=1" --rm --shm-size=10G --memory=10G $DOCKER_SHA \
     python /ray/python/ray/rllib/examples/multiagent_two_trainers.py --num-iters=2
 
+docker run  -e "RAY_USE_XRAY=1" --rm --shm-size=10G --memory=10G $DOCKER_SHA \
+    python /ray/python/ray/rllib/examples/cartpole_lstm.py --stop=75
+
 # No Xray for PyTorch
 docker run --rm --shm-size=10G --memory=10G $DOCKER_SHA \
     python /ray/python/ray/rllib/train.py \