You signed in with another tab or window. Reload to refresh your session.You signed out in another tab or window. Reload to refresh your session.You switched accounts on another tab or window. Reload to refresh your session.Dismiss alert
error
in get_greedy_action
return torch.argmax(all_q.sum(dim=0), dim=1, keepdim=True)
IndexError: Dimension out of range (expected to be in range of [-1, 0], but got 1)
Match3 env,It seems like Match3VectorObs is only receiving 2 inputs instead of the expected 4 inputs. Additionally, I have a question: why is Match3VectorObs in Unity for 4 agents, but env.behavior_specs["Match3VectorObs?team=0"].observation_specs only retrieves two?
error
line 374, in _assert_worker_can_restart
raise exception
mlagents_envs.exception.UnityActionException: The behavior Match3VectorObs?team=0 needs a continuous input of dimension (4, 0) for (, ) but received input of dimension (2, 0)
The text was updated successfully, but these errors were encountered:
Describe the bug
3DBall env,It seems that there is an incompatibility issue with the shape when obtaining the action due to some torch version reasons
mlagents-learn dqn_basic.yaml --env="3DBall" --run-id=3DBall-dqn
config.yaml
behaviors:
3DBall:
trainer_type: dqn
hyperparameters:
learning_rate: 0.0003
learning_rate_schedule: constant
batch_size: 64
buffer_size: 50000
tau: 0.005
steps_per_update: 10.0
save_replay_buffer: false
exploration_schedule: linear
exploration_initial_eps: 0.8
exploration_final_eps: 0.05
network_settings:
normalize: false
hidden_units: 20
num_layers: 2
vis_encode_type: simple
reward_signals:
extrinsic:
gamma: 0.99
strength: 1.0
keep_checkpoints: 5
max_steps: 500000
time_horizon: 10
summary_freq: 1000
envs
Version information:
ml-agents: 1.0.0,
ml-agents-envs: 1.0.0,
Communicator API: 1.5.0,
PyTorch: 2.2.1
error
in get_greedy_action
return torch.argmax(all_q.sum(dim=0), dim=1, keepdim=True)
IndexError: Dimension out of range (expected to be in range of [-1, 0], but got 1)
Match3 env,It seems like Match3VectorObs is only receiving 2 inputs instead of the expected 4 inputs. Additionally, I have a question: why is Match3VectorObs in Unity for 4 agents, but env.behavior_specs["Match3VectorObs?team=0"].observation_specs only retrieves two?
mlagents-learn dqn_basic.yaml --env="Match3" --run-id=Match3-dqn
config.yaml
default_settings:
trainer_type: dqn
hyperparameters:
learning_rate: 0.0003
learning_rate_schedule: constant
batch_size: 64
buffer_size: 50000
tau: 0.005
steps_per_update: 10.0
save_replay_buffer: false
exploration_schedule: linear
exploration_initial_eps: 0.8
exploration_final_eps: 0.05
network_settings:
normalize: false
hidden_units: 20
num_layers: 2
vis_encode_type: match3
reward_signals:
extrinsic:
gamma: 0.99
strength: 1.0
keep_checkpoints: 5
max_steps: 500000
time_horizon: 10
summary_freq: 1000
envs
Version information:
ml-agents: 1.0.0,
ml-agents-envs: 1.0.0,
Communicator API: 1.5.0,
PyTorch: 2.2.1
error
line 374, in _assert_worker_can_restart
raise exception
mlagents_envs.exception.UnityActionException: The behavior Match3VectorObs?team=0 needs a continuous input of dimension (4, 0) for (, ) but received input of dimension (2, 0)
The text was updated successfully, but these errors were encountered: